Hands-on Training Sessions
– Taught by World-Class Data Scientists –
Learn the latest data science concepts, tools, and techniques from the best. Forge a connection with these rock stars from industry and academia, who are passionate about molding the next generation of data scientists.
WHY ODSC?
Form a working relationship with some of the world’s top data scientists for follow up questions and advice.
Access to 50+ talks and workshops.
Recordings of workshop and talks sessions for later review.
Equivalent training at other conferences costs much more.
Professionally prepared learning materials, custom tailored to each course.
Opportunities to connect with other ambitious like-minded data scientists.
Meet some of our World Class Instructors
50+ More Training & Workshop Sessions Coming Soon. Check full speaker lineup here.
Training | Data Visualization | Machine Learning | Beginner-Intermediate
Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this 3.5 hr tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.
Upon completing this tutorial, you will be:
– familiar with how to use the NetworkX and nxviz Python packages for modelling and rationally visualizing networks,
– able to load node and edge data from a Pandas dataframe,
– familiar with object-oriented and matrix-oriented representations of graphs,
able to find paths between nodes, interesting structures in graphs, and projections of bipartite graphs.
– (if time permits) able to use matrix operations to simulate diffusion of information on networks..more details
Eric is an Investigator at the Novartis Institutes for Biomedical Research, where he solves biological problems using machine learning. He obtained his Doctor of Science (ScD) from the Department of Biological Engineering, MIT, and was an Insight Health Data Fellow in the summer of 2017. He has taught Network Analysis at a variety of data science venues, including PyCon USA, SciPy, PyData, and ODSC, and has also co-developed the Python Network Analysis curriculum on DataCamp. As an open-source contributor, he has made contributions to PyMC3, matplotlib, and bokeh. He has also led the development of the graph visualization package nxviz, and a data cleaning package pyjanitor (a Python port of the R package).
Training | Open-source | Kick-starter | Beginner
Recommendation systems are fundamental to essentially every business on the planet. From providing the shows and movies you should watch, the books and articles you should read, the products you should purchase, or the people you should date making good recommendations can make or break your business. This workshop will give a general introduction to recommendation systems, as well as introduce practical tools for you to apply recommendation systems to your own use cases. Though the primary focus of this workshop will be to understand the different methods you might use to make recommendations, we will also touch briefly on common difficulties with building effective recommendation systems, how to deploy your recommendations, and how to evaluate whether your recommendations are effective…more details
Josh has been sharing his passion for data science with others more than a decade. He found his passion for teaching while pursuing a PhD in Statistics at Iowa State University. He then went on to teach graduate and undergraduate courses as a part of the Business Analytics program at the University of Colorado. Before jumping full time into industry, he worked as the Curriculum Lead for the School of Data Science at Udacity, where he built data science courses that train data scientists around the world.
In industry, Josh has used his skills to implement data science and machine learning solutions across industries from tax and auditing to healthcare. He currently works as a Data Scientist at NerdWallet, where he partners with product and marketing to help consumers make all the right money moves.
Full-Day Training | MLOps & Data Engineering | Machine Learning | Intermediate-Advanced
In this tutorial we will consider the symbiosis of graphs and Machine Learning including the following topics:
*Graph-based Feature Engineering and Graph Algorithms*
We will start by looking at popular graph algorithm and their value for feature engineering for Machine Learning models.
*Graph Embeddings and Graph Neural Networks*
Utilizing graphs as input to Neural Networks is a very powerful combination. In this part, we will consider different Embedding strategies and the field of Graph Neural Networks.
*Graph-based Machine Learning Metadata*
We know about the value of high quality and quantity for building high-quality machine learning models, but for operating a production-grade machine learning pipeline Metadata is equally important. In this part, we will look at leveraging graphs to capture metadata and provenance information of our machine learning ecosystem...more details
Jörg Schad is Head of Development and Machine Learning at ArangoDB. In a previous life, he has worked on or built machine learning pipelines in healthcare, distributed systems at Mesosphere, and in-memory databases. He received his Ph.D. for research around distributed databases and data analytics. He is a frequent speaker at meetups, international conferences, and lecture halls.
Training
Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.
Bootcamp | Kick-starter | All Levels
The emergence of data science as a discipline has impacted businesses in a range of different ways. One primary impact has been to elevate the use of data in decision-making by using statistical methods to assess the ever-growing datasets companies are collecting. This workshop will review and introduce statistical techniques and touch on more advanced methods for dealing with noisy data and applying real-world constraints to analyses. This workshop assumes a working knowledge of standard statistical methods and will aim to connect theory to practice using real-world examples.
Lesson 1: Descriptive statistics and exploring data statistically
– (Re)familiarize yourself with basic descriptive statistics
– Use simple data exploration techniques to identify problems and limitations of a new dataset
Lesson 2: Statistical analyses...more details
Andrew is a Ph.D. Astrophysicist who made the switch from academia to data science (via the Insight Data Science program) in 2014. He was the first data scientist hired at Greenhouse Software where he has worked on many internal data science projects and a few customer-facing data-powered product features. Andrew lives in New Jersey with his wife and son.
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Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.
Training | Data Visualization | Kick-starter | All Levels
In this complete hands-on training session, you will learn to turn your data into interactive dashboards, how to create stories with data and share these dashboards with your audience. We will begin with a quick refresher of basics about design and information literacy and discussions about practices for creating charts and storytelling utilizing best visual practices. Whether your goal is to explain an insight or let your audience explore data insights, using Tableau’s simple drag-and-drop user interface makes the task easy and enjoyable.
In this session, we’ll cover intermediate and advanced tableau functionality:
1. Database connectivity
2. Blending and joins using multiple data sources, web connectors and plugins,
3. Perform data analyses and create graphs from a real-world dataset, using Tableau Public (free to use)
4. Deeper Analysis – Trends, Clustering, Distributions, and Forecasting
5. Table Calculations, Sets, Filters, Level of Detail expressions, Parameters
6. Spatial analytics – Using Maps and Geocoding
7. Right and Wrong way to build Dashboards and Best Practices…more details
Nirav Shah is the Founder of OnPoint Insights, data analytics, software services, and staff augmentation consultancy based in Boston. He has 15 years of industry experience – mainly in consulting on data analytics, big data modeling, process analytics, and real-time data solutions, and training customers in data analytics, dashboards, and data visualization.
He consults and teaches in applying data analytics for manufacturing, operations, supply chain, process control strategies with clients to improve the manufacturing process and operational efficiency. He has implemented real-time process monitoring data analytics and fault detection systems for leading biopharma customers and clients from other industries such as chemical, pulp, and paper, food and beverages. He helps customers in providing better process insights using data-driven solutions.
He is also an Adjunct Professor at the University of Massachusetts in Boston where he teaches Engineering Process Analytics, a graduate-level class in the Engineering Department, teaches Business Analytics and Dashboard Visualization at a technical college and conducts BootCamps and Workshops at General Assembly Boston. He has taught courses and conducted workshops to industry clients on Multivariate Data Analysis for ten years. He has spoken at various conferences ( ODSC East Boston, ODSC India, Global AI).
He completed his dual Masters in Chemical and Computer Engineering from the University of Massachusetts and an MBA in Entrepreneurship from Babson College.
Training | Open-source | Kick-starter | Beginner
The goal of this tutorial is to guide new learners into R. We’ll introduce how to program in R by cleaning and processing data as the use case. This will help transition new learners who work with data in spreadsheets but want to utilize the power of R in their work.
The only pre-requsites needed for the tutorial is having R installed with the `tidyverse`, `data.table` packages installed. The basics of the R language will be taught from the perspective of data processing. The course will be mainly focused around the “tidyverse”, however, where appropriate, we will draw similarities to base R and data.table for completeness...more details
Daniel is a PhD candidate in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, currently working in the Social and Decision Analytics Laboratory in the Biocomplexity Institute at Virginia Tech. His current interests are in understanding how attitudes change and spread within social networks as well as performing analytics for precision medicine.
Daniel received his MPH in the Department of Epidemiology at Columbia University and hold a BA in Psychology – Behavioral Neuroscience with minors in Biology and Computer Science from the Macaulay Honors College at CUNY Hunter College.
Daniel enjoys teaching and volunteers some of his time to Software Carpentry by teaching, serving on the Mentoring subcommittee, and chair the Assessment subcommittee.
Training | MLOps & Data Engineering | ML for Programmers | Beginner-Intermediate
By attending this workshop, you will learn how you can become a contributor to Apache Airflow project. You will learn how to setup development environment, how to pick your first issue, how to communicate effectively within the community and how to make your first PR – experienced committers of Apache Airflow project will give you step-by-step instructions and will guide you in the process. When you finish the workshop you will be equipped with everything that is needed to make further contributions to the Apache Airflow project.
You will have a chance to pick a simple issue and get it through the whole process – starting from implementation, testing, going through review process and hopefully merging the changes. We will pick some simple changes for you so that you have a chance to implement it during the workshops...more details
Tomek is a Software engineer at Polidea, Apache Airflow committer and book lover. He fancies functional programming because he is graduated mathematician. Every day he tries to make the world a better place.
As the CTO Jared built software house 10-fold: from 6 to 60 people. After a few years of being the CTO, he decided to go back to a full-time engineering role and he works as a Principal Software Engineer in his company (and is super happy about it). Jarek has been working as an engineer in many industries – Telecoms, Mobile app development, Google, Robotics and Artificial Intelligence, Cloud and Open Source data processing. Jarek is currently PMC in the Apache Airflow project.
Training | Kickstarter | Machine Learning | Beginner
Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detect the Higgs boson, machine learning excels at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to “learn” a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formulating a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. This workshop should give you all the necessary background to start using machine learning yourself…more details
Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.
Traning | Data Vizualization | ML for Programmers | Beginner-Intermediate
This workshop will take one through the steps associated with an end-to-end machine learning campaign: data retrieval; data curation; model construction, evaluation, selection and interpretation; and reporting. Particular attention will be paid to reporting, i.e., building a narrative. Examples will be presented demonstrating how one might generate multiple output formats (e.g., HTML pages, presentation slides, PDF documents) starting with the same code base.
The workshop will have three main foci:
1. infrastructure: instantiating the computational environment; loading packages; loading data
2. computation: data curation, transformation, and analysis; model construction, evaluation, and interpretation
3. communication: creating tables, charts, and graphs; weaving all components into data narrative…more details
Paul Kowalczyk is a Senior Data Scientist at Solvay. There, Paul uses a variety of toolchains and machine learning workflows to visualize, analyze, mine, and report data; to generate actionable insights from data. Paul is particularly interested in democratizing data science, working to put data products into the hands of his colleagues. His experience includes using computational chemistry, cheminformatics, and data science in the biopharmaceutical and agrochemical industries. Paul received his Ph.D. from Rensselaer Polytechnic Institute and was a Postdoctoral Research Fellow with IBM’s Data Systems Division.
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Matt is currently Managing Partner and Principal Data Scientist at BetaVector. His full-time professional data work spans finance, education, consumer-packaged goods, and politics and he earned General Assembly’s 2019 “Distinguished Faculty Member of the Year” award. Matt earned his Master’s degree in statistics from Ohio State. Matt is passionate about responsibly putting the power of machine learning into the hands of as many people as possible and mentoring folx in data and tech careers. Matt also volunteers with Statistics Without Borders and currently serves on their Executive Committee as the Marketing & Communications Director.
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Josh Benamram is Co-Founder of Databand, an APM and observability solution for data engineering teams. Josh comes from a background in Product Management and VC in the data infrastructure space. Prior to Databand he was a Product Manager at Sisense, a business analytics platform provider, where he led Sisense’s database, ETL, and predictive analytics product components. Before Sisense, Josh was a VC investor at Bessemer Venture Partners, focusing on machine learning and data infrastructure investments.
Evgeny is Cofounder of Databand, an APM and observability solution for data engineering teams. Evgeny is a data architect and engineer by background. Prior to Databand, Evgeny was first employee, data architect, and team lead at Crosswise, a big data startup acquired by Oracle Data Cloud. Before Crosswise and ODC, Evgeny was a senior developer, software engineering team lead, and researcher at various startups.
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Joy Payton is a cloud engineer, data scientist, and adjunct professor who specializes in helping biomedical professionals conduct reproducible computational research. In addition to moving medicine forward through principles of open science and reproducibility, Joy also enjoys teaching citizen scientists how to use public data repositories to understand their own communities better and advocate for change from a data-centric perspective. Her various roles allow Joy to lead efforts to teach people how to write their first line of code and help anyone who’s interested climb the data science learning curve. Currently employed by the Children’s Hospital of Philadelphia and Yeshiva University, Joy is always open to hearing about open-source, data-centric volunteer opportunities for herself and her students.
Tutorial | Open-source | All Levels
Responsible AI is an umbrella term for many themes associated with the intersection of ethics and AI. One reasonable enumeration is Microsoft’s 6 Principles for AI development: Four core principles of fairness, reliability/safety, privacy/security, and inclusiveness, underpinned by two foundational principles of transparency and accountability. For this presentation, we focus on Transparency (Interpretability), Fairness and Inclusiveness, and Privacy as major principles of responsible AI and cover best practices and state-of-the-art open source toolkits and offerings that target researchers, data scientists, machine learning developers, and business stakeholders to be able to build trustable, more transparent AI systems.
Attendees will leave the session with the basic understanding of responsible AI principles, best practices and open source tools around responsible development and deployment of AI systems. They will be able to incorporate the introduced tools and products in their machine learning life cycle, running them on their previously-trained models to understand the factors that went into their model predictions, and verify their model fairness across protected attributes and mitigate the existing bias...more details
Mehrnoosh Sameki is a senior technical program manager at Microsoft, responsible for leading the product efforts on machine learning interpretability and fairness within the Azure Machine Learning platform. She earned her PhD degree in computer science at Boston University, where she currently serves as an adjunct assistant professor and lecturer, offering courses in responsible AI. Previously, she was a data scientist in the retail space, incorporating data science and machine learning to enhance customers’ personalized shopping experiences.
Minsoo is a Program Manager in the Responsible AI team at Microsoft focusing on building out offerings for the OSS Interpretability Toolkit and its integration into Azure Machine Learning Platform. She recently graduated from Microsoft’s pilot AI rotation program as one of three first PMs in the first cohort working on a variety of ML/AI application projects within Microsoft to accelerate its adoption of the AI-first initiative. She has Bachelor’s degrees in Applied Math and Painting from Brown University and Rhode Island School of Design (RISD). Coming from an interdisciplinary background with experience in building models and applications, analyzing data, and designing UX, she is looking to work in the intersection of AI/ML, design, and social sciences to empower data practitioners to work ethically and responsibly end-to-end.
Ehi Nosakhare is a Data and Applied Scientist in the AI development and acceleration program at Microsoft. She designs, develops and leads the implementation of machine learning (ML) solutions in application projects across Microsoft’s products and services. She is currently focused on developing a toolkit that enables text interpretability and machine learning transparency more broadly. Prior to Microsoft, she completed a Ph.D. in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT). She is very passionate about using ML to solve real-world problems and studying the ethical implications of ML/AI. In her spare time, she enjoys reading and re-learning to play the cello.
Workshop | NLP | Beginner-Intermediate
In this session, I’ll start by introducing the recent breakthroughs in NLP that resulted from the combination of Transfer Learning and Transformer architectures. Then, we’ll learn to use the open-source tools released by HuggingFace like the Transformers and Tokenizers libraries and the distilled models.
Learning outcomes: understanding Transfer Learning in NLP, how the Transformers and Tokenizers libraries are organized and how to use them for downstream tasks like text classification, NER and text generation...more details
Thomas leads the Science Team at Huggingface Inc., a Brooklyn-based startup working on Natural Language Generation and Natural Language Understanding.
After graduating from Ecole Polytechnique (Paris, France), he worked on laser-plasma interactions at the BELLA Center of the Lawrence Berkeley National Laboratory (Berkeley, CA). Got accepted for a PhD at MIT (Cambridge, MA) but ended up doing his PhD in Statistical/Quantum physics at Sorbonne University and ESPCI (Paris, France), working on superconducting materials for the French DARPA (DGA) and Thales.
Thomas is interested in Natural Language Processing, Deep Learning, and Computational Linguistics. Much of his research is about Natural Language Generation (mostly) and Natural Language Understanding (as a tool for better generation).
Workshop | Intermediate
Effective predictive modeling projects follow the analytics life cycle, from data and discovery to deployment and decisions. Data scientists use a variety of tools, both commercial and open-source, to collaborate and develop enterprise applications of analytics and artificial intelligence. SAS Viya provides a unified platform to perform all these from one graphical user interface or through programming APIs. In this workshop, you will load data into memory, prepare input variables for modeling and build complex analytics pipelines to demonstrate powerful machine learning models. Need to integrate open source models? No problem. We’ll show you how you to do that and deploy any model. Then you can save and package the best performing model for deployment while keeping the ability to retrain it on new data…more details
Jordan Bakerman holds a Ph.D. in statistics from North Carolina State University. His dissertation centered on using social media to forecast real world events, such as civil unrest and influenza rates. As an intern at SAS, Jordan wrote the SAS Programming for R Users course for students to efficiently transition from the R to SAS using a cookbook style approach. As an employee, Jordan has developed courses demonstrating how to integrate open source software within SAS products. He is passionate about statistics, programming, and helping others become better statisticians.
Ari Zitin holds bachelor’s degrees in both physics and mathematics from UNC-Chapel Hill. His research focused on collecting and analyzing low energy physics data to better understand the neutrino. Ari taught introductory and advanced physics and scientific programming courses at UC-Berkeley while working on a master’s in physics with a focus on nonlinear dynamics. While at SAS, Ari has worked to develop courses that teach how to use Python code to control SAS analytical procedures, including machine learning and optimization.
Tutorial | Deep Learning | Computer Vision | Beginner-Intermediate
There has been huge advancements in the field of Artificial Intelligence in the last decade. While this has been fueled by better compute, more data and cheaper storage, the real success which has revolutionized AI applications is a combination of deep learning and transfer learning. This session will give a gentle introduction to deep learning, deep transfer learning, cover its scope, advantages, applications and limitations. We will then do a deep-dive into three industry focused use-cases pertaining to Semiconductors and Manufacturing where we have leveraged a combination of deep transfer learning and traditional computer vision to solve these challenging, yet interesting problems at nanoscale...more details
Dipanjan (DJ) Sarkar is a Data Science Lead at Applied Materials, leading advanced analytics efforts around computer vision, natural language processing and deep learning. He is also a Google Developer Expert in Machine Learning. He has consulted and worked with several startups as well as Fortune 500 companies like Intel and Open Source organizations like Red Hat. He primarily works on leveraging data science, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, computer vision and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. Dipanjan is also a published author, having authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing, and Deep Learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.
Sachin is a Deputy Director at Applied Materials leading efforts in Data Science and Advanced Analytics with a team of data scientists, working to provide innovative solutions with a focus on AI enablement in various products and systems for the semiconductor equipment manufacturing industry. He has a BS in Information Technology and over 15 years of diverse experience in the Industry around Software, Data Science and Artificial Intelligence.
Workshop | MLOps & Data Engineering | All Levels
Many enterprises found it challenging to scale AI solutions beyond pilots. The root cause of the challenges stem from the paradigm shift in how AI systems are built and work from the traditional systems. There are big gaps between AI algorithm frameworks on the market and enterprise scale AI systems; and between skill requirements and practice in developing AI systems and enterprise solutions. Talents that can bridge the gap are scarce. Wenju is a project at Futurewei that tackles such challenges. It is a platform that helps enterprises of any size to quickly and cost effectively build end-to-end AI solutions in hybrid and multi-cloud environments. The platform adds new architectural layers on top of existing technology stacks to address key requirements and concerns of enterprise AI systems, and provides components and new building blocks that empowers the development and operations team. It helps to reduce skills required to build AI solutions, while improving productivity by enabling end-to-end automation...more details
Changfeng Charles Wang is a technology consultant at Futurewei. He is also a serial entrepreneur and expert in AI & big data with over 20+ experience. His work spans across theoretical foundations, technology platforms, and real-world vertical applications surrounding machine learning and data. He is the founder of Boston Data Science, LLC, an AI and big data technology and strategy firm. Previously, he was the founder of Adelphic, an industry-leading mobile and cross-screen programmatic platform that uses AI to completely automate real-time advertising transactions, serving top advertising agencies and big brands across the globe. He began his career at Thinking Machines (Acquired by Oracle), where he built the world’s first commercial parallel Neural Networks and core Machine Learning platform. Other products that he built are now part of Apple (iAd), IBM (InfoSphere), Facebook (Atlas), and Nokia.
Workshop | MLOps & Data Engineering | Intermediate
Kedro is a development workflow tool open sourced by QuantumBlack, a McKinsey company. Many data science teams have started using the library for their pipelines but are unsure how to integrate with other model tracking tools, such as MLflow. In this tutorial, we will give an overview of Kedro and MLflow and demo how to leverage the best of both. The goal of this session is to demonstrate how Kedro and MLflow fit together in a scalable AI architecture. To start, we will give an overview of Kedro and an overview of MLflow: – What are they used for? – What functionality do they provide? – How do they compare as tools? Next, we will walk through a demo of a Kedro project that has MLflow integrated into it. Finally, we will go over deployment options…more details
Tom is a Junior Principal Data Engineer at QuantumBlack, a McKinsey Company. Prior to consulting, Tom was CTO and co-founder of Commandiv, a wealth management startup.
Workshop | Deep Learning | Beginner-Intermediate
PyTorch has become the dominant tool used in machine learning research. As for a more concrete plan, the session will be centered around extensibility, performance and model packaging, and will be presented as a case study of potential modifications that could be applied to the OpenNMT-py repository. Firstly, because we will care about investigating performance, we’ll kick off the session with a short description of the library’s execution model and best practices for benchmarking PyTorch code. Then, we’ll move on to custom extensions — a feature that allows loading plugins at run-time to, for example, expose third-party libraries or hand-written C or CUDA code. Finally, we will talk a fair amount about the JIT compiler which aims to both optimize the run-time performance of your models and enable easy export and later deployment on mobile devices….more details
Adam is an author and maintainer of PyTorch. He has worked with large organizations like Facebook AI Research, NVIDIA and Google, despite the fact that he has graduated from the Master’s program in Computer Science at the University of Warsaw only last year. Currently, he is also finishing his second major in Mathematics. His general interests include graph theory, programming languages, numerical computing and machine learning.
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Veysel Kocaman is a Senior Data Scientist and ML Engineer at John Snow Labs and has a decade long industry experience. He is also pursuing his PhD in CS as well as giving lectures at Leiden University (NL) and holds an MS degree in Operations Research from Penn State University. He is affiliated with Google as a Developer Expert in Machine Learning.
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Vishal Hawa is a Principal Scientist at The Vanguard Group, where he works closely with marketing managers to design attribution, propensity, and attrition modeling. Vish has over 15 years of experience in the retail and financial services industries. He has training in executive management from the Wharton School and holds postgraduate degrees in information sciences, statistics, and computer engineering from the Indian Statistical Institute.
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Sanjay Kottaram is a detail-oriented technical leader well-versed in design, development, and operations of distributed, highly concurrent, data ingestion and processing systems. Extensive experience in first-of-a-kind multi-year projects from inception to realization. Focused on cognitive applications and devices, machine learning (ML), artificial intelligence (AI), Blockchain, edge computing. Real-world business common sense, proficient in collaborating with solution engineering and reference architecture teams to ensure alignment with enterprise architecture standards and governance processes. Partner with product, architecture, and operations teams to design and implement engineering solutions which support roadmap. Skilled in adapting technologies to specific industries, domains, and applications in scalable manner.
Tutorial | Machine Learning | Data Visualization | Beginner-Intermediate
I will cover what snakemake is, how to think about your problem in this type of pipeline, walk through examples of pipelines that can perform RNA-sequencing analysis, and discuss a FELIX, a program on which I have used Snakemake. FELIX is a program whose goal is to identify genetically engineered organisms in complex samples. For Felix, I use Snakemake to create pipelines that have been deployed onto clusters to process 100 samples (100s of Gb of data) and perform over a thousand hours of compute time in about two days. The pipeline includes alignment of DNA sequences, assembling sequences that contain signs of engineering into larger constructs, identifying the makeup of those constructs, and creating visualizations so that subject matter experts can quickly identify if a sample has been genetically engineered or not…more details
Laura Seaman is a Senior Machine Intelligence Scientist at Draper where she applies machine learning and bioinformatics algorithms to a variety of applications. Dr. Seaman’s graduate work focused on using genetic data to identify alterations to the genomic structure in cancer. She is currently using data science for many applications including analysis of financial networks and identification of genetically engineered organisms. Dr. Seaman has a Bachelor of Science in Biological Engineering from the Massachusetts Institute of Technology, a Masters of Arts in Statistics from the University of Michigan, and a Doctor of Philosophy in Bioinformatics from the University of Michigan.
Workshop | Machine Learning | Beginner-Intermediate
Want to learn more about trends like AI, IoT and wearable tech? In this workshop, we will cut through the hype by building a “smart” fitness tracker using your own mobile device. We’ll do hands-on exercises: you’ll acquire data from sensors, design a step counter and train a human activity classifier. You will leave motivated and ready to use machine learning and sensors in your own projects!
The goal for this session is twofold: show our participants how easy it is to get started with Machine Learning and wearables and set them up for success to take on challenging projects in this domain. A clear instruction sheet will guide the participants through exercises ranging from beginner to intermediate level. This workshop is highly interactive with hands-on exercises in which the participants will create a “smart” wearable device and test it out. We aim to demonstrate how powerful applications can be built when we combine machine learning, sensor analytics, and IoT. Participants will use the free MATLAB Mobile App to access built-in sensors and analyze data on their mobile devices...more details
Louvere Walker-Hannon is a MathWorks Application Engineer, who provides direction and recommendations on technical workflows for various applications. Specifically, she assists with the following topics image processing, computer vision, machine learning, deep learning, geospatial analysis, and data analytics when discussing technical workflows. Louvere has worked in three different engineering roles throughout her 20 year career while at MathWorks. A prominent theme in her career is communication of technical concepts to various audiences and being involved with STEM education. She was also a Program Presenter, in this role she was the lead educator of STEM topics for Cahners ComputerPlace within the Boston Museum of Science. Louvere is a member of the Society of Women Engineers (SWE) and the National Society of Black Engineers (NSBE). She has a long history of serving as a STEM mentor. Louvere volunteers with Black Girls Code and the Society of Women Engineers. She has a bachelor’s degree in Biomedical Engineering and a master’s degree in Geographic Information Technology with a specialization in Remote Sensing. Louvere has presented and continues to present at several STEM related conferences on various topics. She recently presented her work on a Natural Language Processing application at the 100th American Meteorological Society in January 2020.
Maria Gavilan-Alfonso is a Senior Online Content Developer at MathWorks, where she helps integrating MATLAB and Simulink in online learning experiences worldwide. She is one of the developers and instructors of the Coursera specialization “Practical Data Science with MATLAB.” Maria holds a MSc in Aerospace Engineering from Purdue University, a BSc in Physics from Universidad Nacional de Colombia, and she’s currently an MBA candidate from University of Illinois at Urbana-Champaign. In her professional career Maria has supported and trained engineers using simulation software for aerodynamics, aeroacoustics, thermal management and flight dynamics. Her current interests are focused on autonomous aerial vehicles and machine learning techniques applied to aerodynamics. Maria also volunteers in multiple events looking to increase the representation of women in science and engineering.
Vaidehi is a principal engineer at MathWorks with over 10 years of software engineering experience. She leads the efforts to generate embedded efficient deep learning network representations using quantization and model compression techniques. In the past, she has developed software tools for automatic deployment of embedded applications on microprocessors, ECUs and FPGAs. Vaidehi has a bachelor’s degree in Information Systems and master’s degree in Computer Science with a specialization in Machine Learning. Prior to MathWorks, Vaidehi was a software engineer at Bloomberg working on solutions that provide e-communication surveillance. Vaidehi has presented technical talks at the MathWorks Research Summit and the MathWorks Advisory Board meetings.
Tutorial | Research Frontiers | Intermediate-Advanced
Thanks to advances in imitation and reinforcement learning techniques, we can now train intelligent agents to accomplish a diverse range of goals. But if we want to create household robots or personal assistants that can take advantage of this diversity, we need to give users some way to tell them what to do! This tutorial will focus on humans’ favorite tools for communicating goals and plans: natural language. We’ll assume basic familiarity with supervised learning and RL, and begin with a review of core machine learning techniques useful for natural language instruction following problems. The body of the talk will focus on modeling techniques for instruction following problems in different kinds of environments and data conditions. We’ll conclude with a survey of other applications for the tools we’ve built, including instruction generation, interpretability, and machine teaching…more details
Jacob Andreas is an assistant professor at MIT and a senior researcher at Microsoft Semantic Machines. His research focuses on language learning as a window into reasoning, planning and perception, and on more general machine learning problems involving compositionality and modularity. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. He has been the recipient of an NSF graduate fellowship, a Facebook fellowship, and paper awards at NAACL and ICML.
Workshop | NLP | Machine Learning | Intermediate-Advanced
This workshop teaches you the use of transformer neural networks and their incarnations (BERT, RoBERTa, GPT-2) for solving real-world natural language use cases. NLP has advanced tremendously over the last few years and BERT is at the forefront of this success having achieved state-of-the-art results on 11 different NLP tasks. For businesses, BERT has unlocked new NLP use cases that have been previously unattainable.
This workshop will teach you what transformers and systems like BERT and GPT-2 are and how to use and modify them for your needs. Organizations have a wealth of unstructured text sources in every line of business, such as employee feedback in human resources, purchase orders and legal documents in contracting and procurement, communication records throughout the org, and many more. Making sense of this information and organizing it into knowledge and actionable insights to improve business outcomes is a key function every data scientist should be aware of…more details
Niels Kasch, PhD is a Founding Partner at Miner & Kasch, a leading AI and Data Science firm. He combines extensive experience in machine learning, analytics, and business knowledge to solve his customers’ key business challenges. An expert in natural language processing, he has developed large-scale, state-of-the-art semantic analysis technologies for combating financial fraud in the finance industry, and text analytics solutions for mitigating risk in the construction industry. He has delivered high quality analytics and data products to Fortune 500 clients and startups alike, covering industries from automotive to utilities. His leadership in framing strategic analytics visions for enterprises has led to the building of several innovative data science teams for his customers.
Workshop | Data Visualization | Deep Learning | Intermediate
For compute-intensive simulation models with high-dimensional input parameters and output spaces, repeated execution of the expensive simulations can become computationally prohibitive and the analysis of the large output is non-trivial. In this talk, I will discuss our recent work on assisting simulation paramter exploration using deep neural networks that won a best paper and an honorable mention awards in the IEEE Visualization conference. To capture the characteristics of
simulation and provide a rapid visualization of the simulation output without huge computation and storage overhead, we develop DNN models to serve as surrogates for prediction of simulation data and visualization images with novel simulation parameters. To achieve these, at simulation time we will collect reduced simulation data and visualization images. Using them as the training data, in one of our recent works we train a generative adversarial networks (GANs) that can take novel simulation and visualization parameters as input to generate new visualization images, which will serve as a quick preview of the simulation output. In another work, we collaborated with computational biologists to design an interactive visual analysis framework, backed by a neural network-based surrogate model that can assist computational biologists in analyzing and visualizing a complex yeast cell polarization simulation model. Our model also allows uncertainty quantification and sensity analysis of the input parameters and simulation output...more details
Han-Wei Shen is a Full Professor at The Ohio State University. He is currently the Associate Editor in Chief of IEEE Transactions on Visualization and Computer Graphics. He is also the chair of the steering committee for IEEE SciVis conference. His primary research interests are scientific visualization and computer graphics. Professor Shen is a winner of National Science Foundation’s CAREER award and US Department of Energy’s Early Career Principal Investigator Award. He has served as an Associate Editor for IEEE Transactions on Visualization and Computer Graphics, a paper chair for IEEE Visualization, IEEE Pacific Visualization, and IEEE Parallel Visualization and Graphics. He is currently on the IEEE Visualization conference executive committee, and IEEE SciVis steering committee. He has published more than 50 papers in IEEE Transactions on Visualization and Computer Graphics and IEEE Visualization conference, the very top journal and conference. He received his BS degree from Department of Computer Science and Information Engineering at National Taiwan University in 1988, the MS degree in computer science from the State University of New York at Stony Brook in 1992, and the PhD degree in computer science from the University of Utah in 1998. From 1996 to 1999, he was a research scientist at NASA Ames Research Center in Mountain View California.
Workshop | Machine Learning | Beginner-Intermediate
This workshop shows how interpretability tools can give you not only more confidence in a model, but also help to improve model performance. Through this interactive workshop, you will learn how to better understand the models you build, along with the latest techniques and many tricks of the trade around interpretability. The workshop will largely focus on interpretability techniques, such as feature importance, partial dependence, and explanation approaches, such as LIME and Shap.
The workshop will demonstrate interpretability techniques with notebooks, some in R and some in Python. Along the way, workshop will consider issues like spurious correlation, random effects, multicollinearity, reproducibility, and other issues that may affect model interpretation and performance. To illustrate the points, the workshop will use easy to understand examples and references to open source tools to illustrate the techniques...more details
Rajiv Shah is a data scientist at DataRobot, where his primary focus is helping customers improve their ability to make and implement predictions. Previously, Rajiv has been part of data science teams at Caterpillar and State Farm. He has worked on a variety of projects from a wide ranging set of areas including supply chain, sensor data, acturial ratings, and security projects. He has a PhD from the University of Illinois at Urbana-Champaign.
Workshop | Machine Learning | Beginner
The Julia language is known for combining performance, ease of use, and flexibility. In the past year, this foundation has continued to expand and Julia users have built on it to push the envelope in several areas of data science and scientific computing. This talk will explain some of these developments and how to take advantage of them. First, we introduced a new composable multi-threading system that makes it easy to take advantage of multiple processor cores in all Julia code and libraries. This has been used, for example, to multi-thread CSV parsing in CSV.jl (written in 100% Julia), bringing its performance on par with the best CSV readers. Julia is at the forefront of the emerging field of scientific machine learning, which combines data-driven learning approaches with traditional modeling techniques such as differential equations. Julia packages now exist for applying scientific ML to a range of real-world problems...more details
Jeff is one of the creators of Julia, co-founding the project at MIT in 2009 and eventually receiving a Ph.D. related to the language in 2015. He continues to work on the compiler and system internals, while also working to expand Julia’s commercial reach as a co-founder of Julia Computing, Inc.
Training | Machine Learning | Open-source | Beginner-Intermediate
The rapid progress in machine learning (ML) and the massive increase in the availability and diversity of data has enabled novel approaches to quantitative investment. It has also increased the demand for the application of data science to develop both discretionary and algorithmic trading strategies.
In this workshop, we will cover popular use cases for ML in the investment industry, and how data science and ML fit into the workflow of developing a trading and investment strategy from the identification and combination of alpha factors to strategy backtesting and asset allocation.
The workshop uses Python and various standard data science and machine learning libraries like pandas, scikit-learn, gensim, spaCy as well as TensorFlow and Keras. The code examples will be presented using jupyter notebooks and are based on my book ‘Machine Learning for Algorithmic Trading’…more details
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
Training | Machine Learning | ML for Programmers | Intermediate
scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This workshop will go beyond the basics and show how to effectively evaluate and tune algorithms. We will also discuss the most important machine learning algorithms that you’re likely to see in practice, how and when to use them, and some details about how they work internally. The session will focus on linear models for classification and regression and tree-based models, including random forests…more details
Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.
Training | Machine Learning | Intermediate
How can an idea from an 18th-century Presbyterian minister be used to estimate the mass density function of galaxies across the Universe? How can a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a Formula 1 race from then 1950’s inspire one of the greatest data science use cases for the Internet of Things? How can a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? This workshop will answer these questions, and more, by presenting several examples of one of the key aptitudes of successful data science practice, which is adaptability. In particular, I will present several well-known algorithms (including some that we would not even call “algorithms”) that may have been adopted for specific use cases or applied in specific business domains, and then I will show how each one can be adapted to a novel use case that may be less obvious, perhaps producing significantly surprising results in some other domain. Plus, I will suggest some new opportunities that may come from interesting combinations of data and algorithms. The point of these exercises is to demonstrate how data scientists can create even more value, beyond that which is expected, from our data assets and our algorithmic talents…more details
Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.
Training | Open-source | MLOps & Data Engineering | Beginner-Intermediate
The intention of this VERY hands on workshop is to get you introduced and playing with some of the great features you never knew about in PostgreSQL. You know, and probably already love, PostgreSQL as your relational database. We will show you how you can forget about using ElasticSearch, MongoDB, and Redis for a broad array of use cases. We will add some nice statistical work with R embedded in PostgreSQL. Finally, we will bring this all together using the gold standard in spatial databases, PostGIS. Unless you have a specialized use case, PostgreSQL is the answer. The session will be very hands on with plenty of interactive exercises.
This is a VERY hands on workshop introducing you to other great features in PostgreSQL to use with PostGIS. We will show you how you can forget about using ElasticSearch, MongoDB, and Redis for a broad array of use cases. We will also work through R embedded in PostgreSQL...more details
Steve is a Partner, and Director of Developer Relations for Crunchy Data (PostgreSQL people). He goes around and shows off all the great work the PostgreSQL community and Crunchy Committers do. He can teach you about Data Analysis with PostgreSQL, Java, Python, MongoDB, and some JavaScript. He has deep subject area expertise in GIS/Spatial, Statistics, and Ecology. He has spoken at over 75 conferences and done over 50 workshops including Monktoberfest, MongoNY, JavaOne, FOSS4G, CTIA, AjaxWorld, GeoWeb, Where2.0, and OSCON. Before Crunchy Data, Steve was a developer evangelist for DigitalGlobe, Red Hat, LinkedIn, deCarta, and ESRI. Steve has a Ph.D. in Ecology.
Training | Machine Learning | R-programming | All Levels
After we build various machine learning models we need to make them accessible to others. We use modeldb so we can store models in a database and plumber to expose our model as a REST API that can be hosted in a Docker container…more details
Daniel is a PhD candidate in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, currently working in the Social and Decision Analytics Laboratory in the Biocomplexity Institute at Virginia Tech. His current interests are in understanding how attitudes change and spread within social networks as well as performing analytics for precision medicine.
Daniel received his MPH in the Department of Epidemiology at Columbia University and hold a BA in Psychology – Behavioral Neuroscience with minors in Biology and Computer Science from the Macaulay Honors College at CUNY Hunter College.
Daniel enjoys teaching and volunteers some of his time to Software Carpentry by teaching, serving on the Mentoring subcommittee, and chair the Assessment subcommittee.
Training | MLOps & Data Engineering | Intermediate
MLOps (Machine Learning operations) is all about helping you bring your machine learning workflows to production. This workshop will show you how to get to production faster with optimized experiences for reproducible model training, packaging, validation, deployment and monitoring. Using an MLOps process you can store, version and track ML assets and collect continuous feedback on model behavior. As a result, you can maintain quality models to keep your business and customers ahead...more details
Shivani Patel is a Program Manager working on MLOps in Azure Machine Learning.
Jordan Edwards is a Principal Program Manager working on MLOps in Azure Machine Learning
Training | Kick-starter | Machine Learning | Beginner
This workshop will provide an overview of how to use machine learning to forecast complex operational problems. This workshop is a hands-on, Python-based, introduction to how machine learning can be used to tackle time-series problems. Topics to be covered include: why time-aware ML problems are different from non-time-aware ML problems; why time-series and forecasting problems in particular are challenging; and how to use to leverage both deep-learning and non-deep-learning approaches to successfully tackle real world problems. This course is a code-based workshop using a variety of Python based tools so familiarity with Python and data science fundamentals will be helpful, but time series experience is not assumed...more details
As Data Science Engineering Architect at DataRobot, Mark designs and builds key components of automated machine learning infrastructure. He contributes both by leading large cross-functional project teams and tackling challenging data science problems. Before working at DataRobot and data science he was a physicist where he did data analysis and detector work for the Olympus experiment at MIT and DESY.
Training | NLP | Deep Learning | Intermediate
Natural language processing (NLP) has become an important field with interest from many important sectors that leverage modern deep learning methods for approaching several NLP problems and tasks such as text summarization, question answering, and sentiment classification, to name a few. In this tutorial, we will introduce several of the fundamental NLP techniques and more modern approaches (BERT, GTP-2, etc.) and show how they can be applied via transfer learning to approach many real-world NLP problems. We will focus on how to build an NLP pipeline using several open-source tools such as spaCy and TensorFlow. Then we will learn how to use the NLP model to search over documents based on semantic relationships. We will use open-source technologies such as BERT and Elasticsearch for this segment to build a proof of concept. In essence, the learner will take away the important theoretical pieces needed to build practical NLP pipelines to address a wider variety of problems in the real world…more details
Elvis Saravia is an educator at Elastic and an independent research scientist. He earned his Ph.D. in Computer Science at National Tsing Hua University. His topics of interest include but are not limited to information retrieval, data analysis, transfer learning, sentiment analysis, intent discovery, natural language modeling, conversational AI, and computational health. Elvis is also lead and editor of dair.ai which aims to democratize AI research, education, and technologies.
Training
Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.
Training | MLOps & Data Engineering | Intermediate
Ray is an open-source distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications from a laptop to a cluster, with an emphasis on ML/AI systems, such as reinforcement learning. It is now used in many production deployments. In this tutorial, we’ll use several hands-on examples to explore the problems that Ray solves and the useful features it provides, such as rapid distribution, scheduling, and execution of “tasks” and management of distributed stateful “serverless” computing. We’ll see how it’s used in several ML libraries (and play with examples using those libraries). You’ll learn when to use Ray and how to use it in your projects…more details
Dean Wampler is an expert in data engineering for scalable streaming data systems and applications of machine learning and artificial intelligence (ML/AI). He is a Principal Software Engineer at Domino Data Lab. Previously he worked at Anyscale and Lightbend, where he worked on scalable ML with Ray and distributed streaming data systems with Apache Spark, Apache Kafka, Kubernetes, and other tools. Dean is the author of “Programming Scala”, “What Is Ray?”, “Fast Data Architectures for Streaming Applications”, “Functional Programming for Java Developers”, and the coauthor of “Programming Hive”, all from O’Reilly. He is a contributor to several open source projects, a frequent conference speaker. He also co-organizes several conferences around the world and several user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington. Find Dean on Twitter: @deanwampler.
Training | Open-source | Beginner-Intermediate
Our goal is for every data scientist to practice data ethics as part of their regular work. In this workshop, you will learn how to make data ethics actionable as a data scientist.
We will present an approach to data ethics based on integrating an ethics checklist into your existing data science workflow. We will be using deon (http://deon.drivendata.org), a lightweight, open-source command line tool designed to easily create and incorporate an ethics checklist into your work. The goal is to enable teams to flexibly carry out the ethical discussions most relevant to them, and to preemptively address issues they may otherwise overlook. There has been much ongoing reflection and dialogue within the data science profession about what ethical principles to abide by, especially in the form of ethical codes and oaths. However, there is often still a gap between those principles and the day-to-day work of data scientists—tenets are either too abstract or too rigid to apply readily. Deon and the checklist framework we present encourages an upfront and ongoing dialogue about the different ethical aspects of your project. By closely integrating data ethics into an ongoing workflow, we as data scientists can cultivate ethical intentionality in our work...more details
Christine Chung is a Data Scientist at DrivenData, whose mission is to bring the power of data science to the social sector. Previously, Christine was a Data Scientist and Machine Learning Engineer at Meetup, where she used machine learning to help people find and build communities in real life. She holds a master’s in Computational Analysis and Public Policy from the University of Chicago.
Emily Miller is a Data Scientist at DrivenData, where she helps mission-driven organizations leverage the power of data science and machine learning to maximize their impact. She is passionate about using data for social good and has previously worked at the Bill & Melinda Gates Foundation, Stanford Center for International Development, and Brookings Institution. She holds a master’s in International Development from The New School and a data science certificate from Metis.
Training | Machine Learning | MLOps & Data Engineering | Intermediate-Advanced
In this workshop, you will learn the best techniques that can be applied manually or automatically to validate and monitor statistical models.
Techniques below will be discussed and demonstrated to perform a full model validation:
Techniques used for initial validation:
Bias and variance
Model selection
Hyperparameters selection
Model interpretation
Adversarial validation o
Techniques used for model monitoring
Bias and variance consistence
Model degradation
Model interpretation consistence…more details
Olivier is a data science expert whose leading field of expertise and cutting-edge knowledge of AI and machine learning led him to support many companies’ digital transformations, as well as implementing projects in different industries. He has led the data team and put in place a data culture in companies like Pratt & Whitney Canada, L’Oréal, GSoft and now Moov AI.
Training
Matt is currently Managing Partner and Principal Data Scientist at BetaVector. His full-time professional data work spans finance, education, consumer-packaged goods, and politics and he earned General Assembly’s 2019 “Distinguished Faculty Member of the Year” award. Matt earned his Master’s degree in statistics from Ohio State. Matt is passionate about responsibly putting the power of machine learning into the hands of as many people as possible and mentoring folx in data and tech careers. Matt also volunteers with Statistics Without Borders and currently serves on their Executive Committee as the Marketing & Communications Director.
Training
Anish Asthana is an experienced Software Engineer with a demonstrated history of working in the computer software industry. Skilled in Python, C++, and cloud technologies such as Kubernetes, Prometheus, and the ELK stack. Strong information technology professional with a Master of Science in Electrical and Computer Engineering Specializing in Data Analytics from Boston University College of Engineering.
Training | Machine Learning | Intermediate
An ML application in production must address all of the issues of modern software development methodology, as well as issues unique to ML and data science. Often ML applications are developed using tools and systems which suffer from inherent limitations in testability, scalability across clusters, training/serving skew, and the modularity and reusability of components. In addition, ML application measurement often emphasizes top level metrics, leading to issues in model fairness as well as predictive performance across user segments.
We discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX), as well as the advantages of containerizing pipeline architectures using platforms such as Kubeflow. Google uses TFX for large scale ML applications, and offers an open-source version to the community. TFX scales to very large training sets and very high request volumes, and enables strong software methodology including testability, hot versioning, and deep performance analysis…more details
A recovering data scientist and TensorFlow addict, Robert has a passion for helping developers quickly learn what they need to be productive.
Irene is a Technical Program Manager on the Research & Machine Intelligence team at Google, where she works to bring machine learning into production pipelines through TensorFlow (TFX). Previously, Irene helped Google’s Counter-Abuse Technology team train machine learning models to fight spam and abuse. Prior to joining Google, she spent over a decade at Microsoft working in several engineering and program management roles across the Enterprise and Operating Systems divisions.
Workshop | MLOps & Data Engineering | Machine Learning | Beginner-Intermediate
The speed in which intelligent decision making can take place is changing the very fabric of the modern industry. From autonomous routing decisions for real-time traffic avoidance to intelligent integrated systems for customer service and assisted analysis or forecasting, the time to correct decision can be a huge differentiator. During the course of this workshop, you will learn how to harness the power of Apache Spark to build up a data ingestion pipeline that can process and learn from streaming data. These learnings can then be applied to make simple decisions using SparkSQL in a parallel streaming system…more details
Scott Haines is a full-stack engineer with a current focus on real-time, highly available, trustworthy analytics systems. He is currently working at Twilio as a Principal Software Engineer on the Voice Insights team, where he helped drive spark adoption and streaming pipeline architectures, and build out a massive stream-processing platform.
Prior to Twilio, he worked on writing the backend Java APIs for Yahoo! Games, as well as the real-time game ranking/rating engine (built on Storm) to provide personalized recommendations and page views for 10 million customers. He finished his tenure at Yahoo! working for Flurry Analytics where he wrote the alerts/notifications system for mobile.
Workshop | Deep Learning | Machine Learning | Advanced
Uncertainty is a critical consideration in any application of deep learning, particularly when safety is on the line. Conventional deep learning approaches lead to models that are overconfident, fragile under domain shift, helpless against adversarial attacks, and unable to convey their implicit understanding of uncertainty in data. This talk will survey methods for quantifying and handling different sources of uncertainty, with a focus on practical, scalable techniques for a variety of common use cases. From principled Bayesian approaches to post-hoc calibration, we’ll cover the theory, tools, techniques, and tips you need to better handle uncertainty in your own deep learning applications…more details
Rebecca Russell is a Senior Machine Learning Scientist in the Perception and Autonomy group at Draper. She received her Ph.D. from MIT and B.S. from Caltech, both in physics. Since joining Draper in 2016, Dr. Russell has lead work on using deep learning to solve technical challenges in a wide variety of domains including robotics, autonomous vehicles, medical image analysis, and cybersecurity. Her current research is focused on creating trustworthy and competency-aware deep learning autonomous systems.
Workshop | ML for Programmers | MLOps & Data Engineering | Intermediate-Advanced
Organizations have long used relational databases for a wide variety of data-intensive applications. Data scientists and analysts need to understand how to work with relational databases, particularly for common data science tasks, such as finding, exploring, analyzing and extracting data within a relational database. SQL is an expressive declarative language designed for working with tabular data. Since its inception, new features have been added that allow for complex expressions and computation, such as regular expressions, sliding window functions, and analytical operations that create non-relational results. Understanding the advanced features of SQL will help data scientists reduce the need for programming custom data manipulation functionality…more details
Dan Sullivan is a Principal Engineer and Architect with New Relic where he focuses on cloud architecture, data science, machine learning, and data architecture. He is the author of six books, most recently on cloud and NoSQL databases, as well as several online courses on machine learning, data science, and cloud computing that have had over 1 million views. Dan holds a doctorate in genetics, bioinformatics, and computational biology. His peer-reviewed research has been published in PloS One, Nucleic Acid Research, Infection and Immunity, and the Journal of Proteomics and Genomics Research.
Training | Deep Learning | Beginner
Over the past couple of years, PyTorch has been increasing in popularity in the Deep Learning community. What was initially a tool used by Deep Learning researchers has been making headway in industry settings. In this session, we will cover how to create Deep Neural Networks using the PyTorch framework on a variety of examples. The material will range from beginner – understanding what is going on “under the hood”, coding the layers of our networks, and implementing backpropagation – to more advanced material on RNNs, CNNs, LSTMs, & GANs. Attendees will leave with a better understanding of the PyTorch framework. Furthermore, a link to a clean documented GitHub repo with the solutions of the examples covered will be provided…more details
Robert loves to break deep technical concepts down to be as simple as possible, but no simpler.
Robert has data science experience in companies both large and small. He is currently Head of Data Science for Podium Education, where he builds models to improve student outcomes and an Adjunct Professor at Santa Clara University’s Leavey School of Business. Prior to Podium Education, he was a Senior Data Scientist at Metis teaching Data Science and Machine Learning. At Intel, he tackled problems in data center optimization using cluster analysis, enriched market sizing models by implementing sentiment analysis from social media feeds, and improved data-driven decision making in one of the top 5 global supply chains. At Tamr, he built models to unify large amounts of messy data across multiple silos for some of the largest corporations in the world. He earned a PhD in Applied Mathematics from Arizona State University where his research spanned image reconstruction, dynamical systems, mathematical epidemiology, and oncology.
Tutorial | Research Frontiers | Open-source | Intermediate
We discuss the core abstractions of ParlAI, including Agents, Worlds, and Messages, and how these allow unified presentation of model training, model evaluation, and model deployment. We highlight the most important models and features, including support for general chit chat, image commenting, and seamless integration with Amazon Mechanical Turk. We show how you can start training a specialized using GPT-2 with just a single command.
We briefly highlight some of our recent advances in dialogue research with deep learning, including how we are teaching chatbots to be personable, knowledgeable, and empathetic. We discuss our methods for robustly evaluating the quality of research chatbots, how we teach models to respond safely. We show off some of our recently deployed research bots, which you can talk to today!
This tutorial is targeted at a technical audience without an existing background in dialogue research. Familiarity with deep learning is recommended, but not required, for the second half of the talk…more details
Stephen Roller is a research engineer at Facebook. He received his PhD in Natural Language Processing at the University of Texas at Austin in 2017. His research interests include natural language processing, deep learning, language generation, and conversational agents.
Workshop | Deep Learning | MLOps & Data Engineering | Advanced
In this tutorial, we will guide you through hands-on examples of integrating tf.keras model with different data input sources through tf.data in production environments, from simple csv/json files to SQL databases and to cloud data warehouse services such as Google Cloud BigQuery. We will also cover Apache Kafka as a data input to illustrate the streaming data pipeline architecture for continuous data processing with machine learning. As a bonus, attendees will learn the basics of distributed machine learning and its production usage…more details
Yong Tang is the Director of Engineering at MobileIron. His most recent focus is on data processing in machine learning. He is a maintainer and the SIG I/O lead of the TensorFlow project. He received the Open Source Peer Bonus Award from Google for his contributions to TensorFlow and is the author of the Kafka Dataset module in TensorFlow. In addition to TensorFlow, Yong Tang also contributes to many other projects for the open-source community. He is a maintainer of Docker, CoreDNS, and SwarmKit. Yong Tang received his PhD in Computer Science & Engineering at the University of Florida.
Workshop | Deep Learning | Intermediate
The participant will be provided with insights and guidance on how to use experiment management from research to production.
In this workshop, we share our experience from numerous deep learning projects. We will discuss topics such as hyperparameter optimization search, data biases, diminishing returns of annotated data and productive use of an experiment management platform.
In addition, we will dedicate the majority of the workshop to hands-on training using Trains – a free open-source experiment manager for machine learning and deep learning projects.
In this section, we will cover the following topics:
*Introduction to Trains – Installing and using trains and trains-agent,
*Advanced use of Trains – Experiments artifacts and hyperparameter search,
*From research to production with Trains – Managing the full life cycle of your project... more details
Moses is a computer vision & deep learning guru and a serial entrepreneur. Moses has more than 20 years of experience making visionary technologies a reality. Prior to Allegro AI, Moses co-founded several start-ups in the computer vision and embedded processing spaces. Moses is an alum of the IDF’s most elite technology unit, has 40 patent and patent applications and 5 academic publications in his name. Moses holds an M.Sc. Cum Laude from Tel Aviv University in Computer Science.
Workshop | MLOps & Data Engineering | Intermediate
In today’s world, the top challenges involved in using AI for real business outcome are managing risks associated with Model’s iterative life cycle, Transparency and Auditability. Enterprise customers struggle to marry the need of being flexible enough to foster a culture that is absolutely necessary for Data Science innovations and at the same time the need for governance involved in AI model creation and operationalization. These challenges lead to lesser number of AI models getting into production and thereby reducing the extent to which the enterprises can leverage the potential of infusing AI in various applications and get huge benefits out of them. The proposed workshop is aimed to show the AI professionals how AI operationalization can be achieved with Governance and Model Risk Management without taking away any flexibility from Data Science innovations. A real life use case would be used to showcase the various phases of AI Operationalization starting from Data Provisioning in a governed but flexible way to the Model Monitoring and feeding back the Model Factsheet for operationalization. Attendees would understand the nuances involved in these steps while developing the use cases themselves…more details
Sourav Mazumder is a Data Scientist Thought Leader, Open Group Distinguished Data Scientist, responsible for technical thought leadership & strategy, technical vitality, and technical enablement in AI and Data Science area for IBM’s clients and also internal people in IBM. Sourav works with enterprise clients from AI strategy development to implementations and productionization particularly focusing on First of a Kind Projects.
Sourav has consistently driven business innovation and values through Methodologies and Technologies related to Artificial Intelligence, Data Science and Big Data transpired through his knowledge, insights, experience and influencing skills across multiple industries including Manufacturing, Insurance, Telecom, Banking, Media, Health Care and Retail industries in USA, Europe, Australia, Japan and India. In last 10 years he has influenced key decision makers of several fortune 500 companies to adopt Artificial Intelligence, Data Science, and Big Data related technologies to address complex business needs. Sourav has also consistently provided directions to and successfully led numerous challenging Artificial Intelligence, Data Science and Big Data projects, applying various related methodologies ranging from Descriptive statistics, Probabilistic Modeling, Algorithmic Modeling, Natural Language Processing, etc., to solve critical business problems. Sourav has also successfully partnered with academia within North America, India, South Africa to mentor the students and enabling them in this area.
Sourav has experience and exposure in working in variety of Artificial Intelligence, Data Science and Big Data related technologies like Watson Open Scale, Watson Studio, Watson Natural Language Processing, Watson Machine Learning, IBM Cloud Pak for Data, Spark, Hadoop, BigSQL, HBase, MongoDb, Solr, System ML, Brunel, Cognos, R, Python, Scala/Java, etc., using them in projects involving phases from creation of Minimum Viable Product to Productionization. Sourav is an Open Source enthusiast and contributes to Open Source regularly.
Sourav consistently publishes papers/blogs/articles in various industry forums. Sourav is co-author, guest editor and chief editor of multiple books in AI, Data Science and Big Data space. Sourav is regularly invited to speak in various Industry conferences, like Spark Summit, IBM Think, Global AI Conference, etc in this subject area.
Tutorial | Open-source | Machine Learning | Intermediate-Advanced
Data science has many powerful tools for using observational data to create models that solve prediction and classification problems. Many of our real world questions are causal, however, and it is well known that predictive models do not answer causal questions. A common attitude is that only controlled experiments can be used to produce causal information, but there are rich fields of statistics and machine learning covering correct methods for doing causal inference and causal discovery from observational data alone. Such methods are increasingly being used in practice, as experimentation is often costly, slow, unethical, or impossible. I will survey current methods for using observational data to answer causal questions, briefly review the theory and assumptions behind those methods, and demonstrate their use with open source software packages…more details
Dr. Kummerfeld is a Research Assistant Professor at the Institute for Health Informatics in the University of Minnesota. His primary research interest is in statistical and machine learning methods for discovering causal relationships, with a special focus on discovering causal latent variable models. His work includes (1) developing novel algorithms for discovering causal relationships and latent variables, (2) proving theorems about the properties of causal discovery and latent variable discovery algorithms, (3) performing benchmark simulation studies to evaluate features of the algorithms that are difficult or impossible to evaluate by other means, and (4) applying these novel algorithms to health data in order to inform the development of new treatments.
Workshop | Deep Learning | Machine Learning | Beginner-Intermediate
We will kick off with an overview of how deep learning models are best published as Docker images on DockerHub, and are best prepared for deployment in local or cloud environments using Kubernetes or Docker.
We highlight the following benefits of such an approach:
– Standardized REST API implementation and application-friendly output format (JSON)
– Abstracting out the complex pre and post processing portions of the model inputs and outputs.
We then demonstrate these concepts with Model Asset Exchange, an open source framework. All these applications and the framework itself are open source and we conclude by inviting contributions and opening the gates for you to be a part of this amazing initiative!..more details
Saishruthi Swaminathan is a developer advocate and data scientist in the IBM CODAIT team, whose main focus is to democratize data and AI through open source technologies. Her passion is to dive deep into the ocean of data, extract insights, and use AI for social good. Previously, she worked as a software developer. On a mission to spread the knowledge and experience, she acquired in her learning process. She also leads education for rural children initiative and organizing meetups focusing on women empowerment. She has a masters in electrical engineering, specializing in data science and a bachelor’s degree in electronics and instrumentation. She can be found on “LinkedIn”:https://www.linkedin.com/in/saishruthi-swaminathan/ and “Medium”:https://medium.com/@saishruthi.tn.
Tutorial | Machine Learning | Deep Learning | Beginner-Intermediate
Powerful machine learning capabilities are available as Cloud AI services. You can embed off-the-shelf models for vision and natural language processing into your applications with APIs. AutoML can help you build a custom model with a minimum of code. If you want to build a custom model yourself, there are serverless tools to develop, train, and deploy your model.
In this session, we will discuss why and when to use AI services versus training your own model. We will cover differences in data requirements, performance, integration, and ease of use. We will also demonstrate how to use ML APIs as well as how to built a custom model with TensorFlow in the cloud...more details
Karl Weinmeister is a Cloud AI Advocacy Manager at Google, where he leads a team of data science experts who develop content and engage with communities worldwide. Karl has worked extensively in machine learning and cloud technologies. He was a contributor to one of the first AI-based crossword puzzle solvers that is still referenced today.
Workshop | Deep Learning
This workshop will provide an overview of intelligent process automation best practices. This workshop will focus on alternating case studies and hands-on group exercises. Topics covered will include: an overview of modern document understanding focusing on the applicability of modern deep learning and transfer learning methods; construction, communication, and measurement of quantitative success metrics; solution architecture for common exotic problem variants such as multi-hop reasoning; and finally focusing on compliance requirements in intelligent process automation brought on by GDPR’s recital 71 and the CCA. This will be a highly interdisciplinary session and should be approachable for anyone with a basic statistics background...more details
Slater Victoroff is the Founder and CTO of indico data solutions, an Enterprise AI solution for unstructured content with an emphasis on text and NLP. He has been building machine learning solutions for startups, governments, and Fortune 100 companies for the past 5 years and is a frequent speaker at AI conferences.
Tutorials | Data Visualization | Open Source | Beginner
Resampling methods like the bootstrap are becoming increasingly common in modern data science. For good reason too; the bootstrap is incredibly powerful. Unlike t-statistics, the bootstrap doesn’t depend on a normality assumption nor require any arcane formulas. You’re no longer limited to working with well understood metrics like means. One can easily build tools that compute confidence for an arbitrary metric. What’s the standard error of a Median? Who cares! I used the bootstrap.
With all of these benefits the bootstrap begins to look a little magical. That’s dangerous. To understand your tool you need to understand how it fails, how to spot the failure, and what to do when it does. As it turns out, methods like the bootstrap and the t-test struggle with very similar types of data. We’ll explore how these two methods compare on troublesome data sets and discuss when to use one over the other…more details