Machine Learning Deep Dive

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Training & Workshop 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 the industry and academia, who are passionate about molding the next generation of data scientists.

Meet  Our 2021 Instructors

Director of Data Analytics and AI SolutionsGoogle Cloud

Valliappa Lakshmanan, PhD

Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud’s data analytics and machine learning products. He founded Google’s Advanced Solutions Lab ML Immersion program and is the author of three O’Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. Follow him on Twitter at @lak_gcp, read articles by him on Medium, and see more details at www.vlakshman.com

Practical Machine Learning on Images(Half-Day Training)

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Adjunct Lecturer | FounderStanford University | Startup on real-time ML

Chip Huyen

Chip Huyen is an engineer and founder working to develop tools that leverage real-time machine learning. Through her work with Snorkel AI, NVIDIA, and Netflix, she has helped some of the world’s largest organizations deploy machine learning systems. She teaches Machine Learning Systems Design at Stanford. She’s also published four bestselling Vietnamese books.

Unifying Development and Production Environments for Machine Learning Projects(Workshop)

Director | Assistant ProfessorUCLA NLP | UCLA CS

Kai-Wei Chang

Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California Los Angeles (UCLA). His research interests include designing robust machine learning methods for large and complex data and building fair, reliable, and accountable language processing technologies for social good applications. Dr. Chang has published broadly in natural language processing, machine learning, and artificial intelligence. His research has been covered by news media such as Wires, NPR, and MIT Tech Review. His awards include the Sloan Research Fellowship (2021), the EMNLP Best Long Paper Award (2017), the KDD Best Paper Award (2010), and the Okawa Research Grant Award (2018). Dr. Chang obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2015 and was a post-doctoral researcher at Microsoft Research in 2016. Additional information is available at http://kwchang.net

Transferable Representation in Natural Language Processing(Tutorial)

Senior Inventive ScientistAT&T Chief Data Office

Noemi Derzsy, PhD

Noemi Derzsy is a Senior Inventive Scientist at AT&T Chief Data Office within the Data Science and AI Research organization. Her research is centered on understanding and modeling customer behavior and experience through large-scale consumer and network data, using machine learning, network analysis/modeling, Spatio-temporal mining, text mining, and natural language processing techniques.
Prior to joining AT&T, Noemi was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. She holds a Ph.D. in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.
Noemi is also involved in volunteering in the data science community. She is a NASA Datanaut and former organizer of the Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group.

Creator and ChairFlyte

Ketan Umare

Ketan Umare is the TSC Chair for Flyte (incubating under LF AI & Data). He is also currently the Chief Software Architect at Union.ai. Previously he had multiple Senior Lead roles at Lyft, Oracle and Amazon ranging from Cloud, Distributed storage, Mapping (map making), and machine learning systems. He is passionate about building software that makes developers and other engineers’ lives easier and provides simplified access to large scale systems. With Flyte, he is trying to bridge gap from ideation to productionization for data and ML pipelines and bring a battle tested approach and structure to the data and ML world.

Deep Dive into Flyte(Half-Day Training)

Chief Innovation OfficerUniversity of California

Neil Sahota

Neil Sahota is an IBM Master Inventor, United Nations (UN) AI Advisor, author of the book Own the A.I. Revolution., and Chief Innovation Officer at UC Irvine. He is a business solution advisor to several large companies and sought-after keynote speaker. Over his 20+ year career, Neil has worked with enterprises on the business strategy to create next generation products/solutions powered by emerging technology as well as helping organizations create the culture, community, and ecosystem needed to achieve success such as the U.N.’s AI for Good initiative. Neil also actively pursues social good and volunteers with nonprofits. He is currently helping the Zero Abuse Project prevent child sexual abuse as well as Planet Home to engage youth culture in sustainability initiatives.

Session on Responsible Ai Coming Soon!

Senior Machine Learning | Developer AdvocatePachyderm, Inc.

Jimmy Whitaker

Jimmy Whitaker is the Machine Learning Developer Advocate at Pachyderm, where he focuses on applied and sustainable practices for implementing the machine learning life cycle. He received his Masters in Computer Science from the University of Oxford, and previously was the Director of Applied Research at Digital Reasoning where he led R&D efforts on Speech Recognition and NLP. He has also co-authored a textbook on the topic, “Deep Learning for NLP and Speech Recognition”.

MLOps: From 0-60 with Pachyderm(Half-Day Training)

Data Scientist Facebook

Clinton Brownley, PhD

Clinton Brownley, Ph.D., is a data scientist at Facebook, where he’s responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook. As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and interactive data visualization courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Visualizing and Analyzing Networks with Python or R (and Javascript)(Tutorial)

Training and Enablement EngineerDomino Data Labs

Andrea Lowe, PhD

Andrea Lowe, PhD is the Training and Enablement Engineer at Domino Data Labs where she develops training on topics including overviews of coding in Python, machine learning, Kubernetes, and AWS. She trained over 1000 data scientists and analysts in the last year. She has previously taught courses including Numerical Methods and Data Analytics & Visualization at the University of South Florida and UC Berkeley Extension. Her conference experience includes a deep learning tutorial at PyCon, 2 invited talks, 21 poster presentations, and 4 chair positions.

Practical Reinforcement Learning for Data Scientists(Workshop)

Co-founder | Adjunct Professor | Apache Drill Project Management Committee ChairStealth Startup, GTK Cyber | University of Maryland

Charles Givre

Charles Givre recently joined JP Morgan Chase works as a data scientist and technical product manager in the cybersecurity and technology controls group. Prior to joining JP Morgan, Mr. Givre worked as a lead data scientist for Deutsche Bank. Mr. Givre worked as a Senior Lead Data Scientist for Booz Allen Hamilton for seven years where he worked in the intersection of cyber security and data science. At Booz Allen, Mr. Givre worked on one of Booz Allen’s largest analytic programs where he led data science efforts and worked to expand the role of data science in the program. Mr. Givre is passionate about teaching others data science and analytic skills and has taught data science classes all over the world at conferences, universities and for clients. Mr. Givre taught data science classes at BlackHat, the O’Reilly Security Conference, the Center for Research in Applied Cryptography and Cyber Security at Bar Ilan University. He is a sought-after speaker and has delivered presentations at major industry conferences such as Strata-Hadoop World, Open Data Science Conference and others. One of Mr. Givre’s research interests is increasing the productivity of data science and analytic teams, and towards that end, he has been working extensively to promote the use of Apache Drill in security applications and is a committer and PMC Member for the Drill project. Mr. Givre teaches online classes for O’Reilly about Drill and Security Data Science and is a coauthor for the O’Reilly book Learning Apache Drill. Prior to joining Booz Allen, Mr. Givre, worked as a counterterrorism analyst at the Central Intelligence Agency for five years. Mr. Givre holds a Masters Degree in Middle Eastern Studies from Brandeis University, as well as a Bachelors of Science in Computer Science and a Bachelor’s of Music both from the University of Arizona. Mr. Givre blogs at thedataist.com and tweets @cgivre.

Staff Field Data ScientistDomino Data Labs

Jennifer Dawn Davis, PhD

Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level.

Practical Reinforcement Learning for Data Scientists(Workshop)

Data Science ManagerEY

Lara Kattan

Lara is a Risk Management Specialist at Federal Reserve Bank of Chicago and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science Bootcamp and built risk models for large financial institutions at McKinsey & Co.

ODSC Speaker - Victor Lo
Head of Data Science & Artificial IntelligenceFidelity Investments

Victor Lo, PhD

Victor has managed teams of quantitative analysts in multiple organizations. He is currently Senior Vice President, Data Science and Artificial Intelligence in Workplace Investing at Fidelity Investments. Previously he managed advanced analytics / data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).

For academic services, Victor is an elected board member of the National Institute of Statistical Sciences (NISS), where he provides guidance to the board and general education to the statistics community. He has also been a visiting research fellow and corporate executive-in-residence at Bentley University, as well as serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS). Victor earned a master’s degree in Operational Research at Lancaster University, UK, and a PhD in Statistics at the University of Hong Kong, and was a Postdoctoral Fellow in Management Science at University of British Columbia. He has co-authored a graduate level econometrics book and published numerous articles in Data Science, Marketing, Statistics, and Management Science literature. and is co-authoring a graduate-level data science textbook titled “Cause-and-Effect Business Analytics.

Tutorial on Uplift Modeling: How to Optimize using Uplift Predictive Models and Uplift Prescriptive Analytics(Tutorial)

Big Data & Data Science & Analytics LeaderCisco

Kumaran Ponnambalam

Kumaran Ponnambalam is an AI and Big Data leader with 15+ years of experience. He is currently the Director of AI for Webex Contact Center at Cisco. He focuses on creating robust, scalable AI platforms and models to drive effective customer engagements. In his current and previous roles, he has built data pipelines, ML models, analytics, and integrations around customer engagement. He has also authored several courses on the LinkedIn Learning Platform in Machine Learning and Big Data areas. He holds an MS in Information Technology and advanced certificates in Deep Learning and Data Science.

 

CEO, Security & Forensics Analyst, Incident Responder | Senior InstructorOne eSecurity | SANS Institute

Jess Garcia

Bio Coming Soon!

Session on Cybersecurity Coming Soon!

Managing Partner, Principal Data Scientist | Distinguished FacultyBetaVector | General Assembly

Matt Brems

Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.

Senior Data Scientist | Data Science InstructorGalvanize Inc (part of Stride)

Rongpeng Li

Ron Li is a data science instructor and senior data scientist at Galvanize, Inc. Before that, He worked on machine learning and knowledge graphs at the Information Sciences Institute. Ron has published a 4.5-star rating book Essential Statistics for Non-STEM Data Analysts. He has also authored/co-authored several academic papers, taught data science to non-STEM professionals as pro bono service, and gave talks at conferences like PyData.

A Complete Real-Time Data Application in 90 Minutes : from Kafka to Streamlit(Workshop)

Senior Software EngineerIterative

Milecia McGregor

Milecia is a senior software engineer, international tech speaker, and mad scientist that works with hardware and software. She will try to make anything with JavaScript first. 

 

Using Reproducible Experiments To Create Better Machine Learning Models(Workshop)

Data ScientistMediaMath

Jayeeta Putatunda

Jayeeta is a Data Scientist with 5+ years of industry experience. She recently led six-week NLP workshops in association with Women Who Code, Data Science track. Jayeeta has also been a speaker at International Conference on Machine Learning (ICML 2020), MLConf EU, WomenTech Global Conference, and Data Summit Connect. She works extensively on NLP projects where she gets to explore a lot of state-of-the-art models and build cool products and firmly believes that data is the best storyteller. Recently, Jayeeta joined MediaMath, a leader in the programmatic AdTech domain. Prior to this, she worked at Indellient, Omnicom, Deloitte, and Volvo Group. Jayeeta is also engaged with some amazing organizations to promote and inspire more women to take up STEM. Jayeeta received her Master of Science in Quantitative Methods and Modeling from City University of New York, NY, and Bachelor of Science in Economics and Statistics from West Bengal State University, India.
Website – https://jayeetap.wixsite.com/helloworld

Build a Question Answering System using DistilBERT in Python(Workshop)

Technology Research DirectorElsevier Labs

Sujit Pal

Sujit Pal is an applied data scientist at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His areas of interests include Semantic Search, Natural Language Processing, Machine Learning and Deep Learning. At Elsevier, he has worked on several machine learning initiatives involving large image and text corpora, and other initiatives around recommendation systems and knowledge graph development. He has co-authored Deep Learning with Keras (https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-keras) and Deep Learning with Tensorflow 2.x and Keras (https://www.packtpub.com/data/deep-learning-with-tensorflow-2-0-and-keras-second-edition), and writes about technology on his blog Salmon Run (https://sujitpal.blogspot.com/).

Data Scientist Derwen, Inc.

Paco Nathan

Known as a “player/coach”, with core expertise in data science, natural language, machine learning, cloud computing; 38+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Advisor for Amplify Partners, IBM Data Science Community, Recognai, KUNGFU.AI, Primer. Lead committer PyTextRank. Formerly: Director, Community Evangelism @ Databricks, and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.

Data Scientist, Software Engineer, Author of Hands-On Data Analysis with PandasBloomberg

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently on in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science. She is currently pursuing a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Introduction to Data Visualization in Python(Half-Day Training)

Lead Strategy Planner for AI EcosystemIntel

Yashesh A. Shroff, PhD

Yashesh Shroff is a Lead Strategy Planner at Intel where he focuses on enabling the AI ecosystem on heterogeneous compute. Recently, as a product manager, he was responsible for the AI and media/game graphics software ecosystem showcasing Intel’s latest-gen graphics architecture (10nm). He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley and a joint MBA from UC Berkeley Haas & Columbia Graduate School of Business.

Lead Data ScientistSilicon Valley Startup in Stealth Mode

Ravi Ilango

Ravi Ilango is a Lead Data Scientist at a silicon valley startup in stealth mode. He is passionate in developing deployable deep learning solutions. Previously he was at StatesTitle and at Foghorn Systems as a Sr. Data Scientist and has over 10 years of experience at Apple as a data Scientist & at Applied Materials in Supply Chain Program Management. Ravi has a Graduate Certificate in Data Mining & Machine Learning from Stanford and completed a Masters Program in Aeronautics and Production Engineering from IIT Madras. He has a BS in Mechanical Engineering, Madras University.

Software Developerembarc Software Consulting GmbH

Oliver Zeigermann

Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.

Developer Relations Engineering ManagerGoogle

Karl Weinmeister

Karl Weinmeister is a Developer Relations Engineering Manager at Google, based out of Austin, Texas. Karl leads a global team of data science and ML engineering experts in the Developer Advocacy organization, who build technical assets and consult with enterprise customers on Artificial Intelligence and Machine Learning. Karl was a contributor to Proverb, an AI-based crossword puzzle solver, which competed at the American Crossword Puzzle Tournament.

Get Started with Time-Series Forecasting using the Google Cloud AI Platform(Workshop)

Sr. Data Science InstructorGalvanize, Inc.

Ryan Kasichainula

Ryan Kasichainula is a data science instructor at Galvanize, Inc, an industry leader in technology education, with data science and software engineering immersive bootcamps. They are also an independent data consultant with experience in the technology, agriculture, energy, and pharmaceutical industries. Ryan enjoys applying data science techniques to a wide variety of domains, and they always have at least one side project in the works, usually in the realm of natural language generation.

Sound Classification and Detection with STFT and CNNs(Workshop)

Senior Engineering ManagerYouTube

Byron Ellis, PhD

Byron has developed large scale data pipelines and processing systems across a variety of industries including Life Sciences, Advertising and Enterprise Software systems. In particular he focuses on distributed systems with low latency requirements for both read and write workloads. Trained as a Statistician with a focus on statistical computing he is also the author of Real-time Analytics published by John Wiley and Sons, which describes both the operational and computational aspects of delivering these systems at scale.

Building a Full Stack Low Latency Analytics Pipeline(Tutorial)

Lead Data ScientistLevi Strauss & Co

Filipa Peleja, PhD

Filipa Peleja is the Levi Strauss & Co Europe Lead Data Scientist at the Data Analytics & AI team. She has always been enthusiastic about technology where she first stepped into the tech world as an undergrad in Computer Science and later Ph.D. in the Machine Learning domain. Her academic accomplishments were recognized with the 1st prize of an industry challenge from a telco and publications in international conferences among which, top tier conferences like SIGIR and ACL. Before joining Levi, Filipa interned at Yahoo! Research and, later, worked as a Sr Data Scientist at Vodafone. Filipa loves to work in an area that she feels very passionate about and also enjoys passing along knowledge, hence, she lectures, supervises projects/thesis for CodeOp, Neueda and Barcelona Technical School.

MLOps… From Model to Production(Workshop)

Center DirectorCenter for Deep Learning in Electronic Manufacturing Inc.

Ajay Baranwal

Ajay K Baranwal is the Center Director at CDLe (Center for Deep Learning in Electronics Manufacturing). He leads applied data science research and development efforts to solve electronics and semiconductor manufacturing problems. Many of his work at the Center relates to machine vision, learning from limited data, and building digital twins to synthesize new data. Before the Center, he has worked on several TensorFlow-based applications, including a Prediction and Diagnostic system, a Document retrieval, and an information extraction system. He holds multiple patents, is coauthor of industrial papers and has been a speaker at related conferences. He is also a co-author of a book named “What’s new in TensorFlow 2.0”.

Data Scientist | Thought Leader | AI & ML Operationalization LeaderIBM

Sourav Mazumder

Sourav Mazumder is an IBM Data Scientist Thought Leader and The Open Group Distinguished Data Scientist. 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. Over the 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 Modelling, Algorithmic Modelling, Natural Language Processing, etc., to solve critical business problems. Sourav has also successfully partnered with academia within North America, India, South Africa to mentor students and enable them in this field. Sourav has experience and exposure in working with a variety of Artificial Intelligence, Data Science and Big Data related technologies such as Watson Open Scale, Watson Natural Language Processing, Watson Machine Learning, IBM Cloud Pak for Data, Spark, Hadoop, BigSQL, HBase, MongoDb, Solr, System ML, Cognos, R, Python, Scala/Java and using them in projects involving phases from creation of Minimum Viable Product to Productionization at an enterprise level. Sourav is an Open Source enthusiast and contributes to Open Source regularly. Sourav holds patents in the Data and AI space (patent profile https://patents.justia.com/search?q=Sourav+Mazumder). 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 (https://www.researchgate.net/profile/Sourav-Mazumder). Sourav is regularly invited to speak in various Industry conferences, like Open Data Science Conference, Spark Summit, IBM Think, Global AI Conference, etc in this subject area. He can be found on Linkedin (https://www.linkedin.com/in/souravmazumder/)

Operationalization of Models Developed and Deployed in Heterogeneous Platforms(Tutorial)

Strategy Engineer | PMC Apache Airflow ProjectAstronomer.io

Daniel Imberman

Daniel Imberman is a PMC of the Apache Airflow project, core contributor of the KubernetesExecutor, and Strategy Engineer at Astronomer.io. He recieved a BS/MS at UC Santa Barbara with a focus in Distributed Systems and Machine Learning and is highly passionate about building the next generation of ML tooling.

Session on MLOps and Airflow Coming Soon!

 

Senior SANS InstructorSANS Institute

Christopher Crowley

Christopher Crowley has 20 years of experience managing and securing networks, beginning with his first job as an Ultrix and VMS systems administrator at 15 years old. Today, Crowley is a Senior Instructor at the SANS Institute and the course author for SOC-Class.com: the culmination of his thoughts on effective cybersecurity operations.
He works with a variety of organizations across industries providing cybersecurity technical analysis, developing and publishing research, sharing expert security insights at conferences, and chairing security operations events. He has provided training to
thousands of students globally.
Crowley holds a multitude of cybersecurity industry certifications and provides independent consulting services specializing in effective computer network defense via Montance®, LLC, based in Washington, DC.

Machine LearningDigits

Hannes Hapke

Hannes Hapke works in machine learning at Digits. Prior, he was a senior machine learning scientist for Concur Labs at SAP Concurfor Concur Labs at SAP Concur, where he explored innovative ways to use machine learning to improve the experience of a business traveler. Hannes has also solved machine learning and ML infrastructure problems in various industries including healthcare, retail, recruiting, and renewable energies. He was recognized as a Google Developer Expert for ML and has co-authored two machine learning publications: “Building Machine Learning Pipeline” by O’Reilly Media and “NLP in Action” by Manning Publications.

NLP Consultant | FounderPractical Linguistics Inc

Zhenya Antić, PhD

Zhenya Antić is an NLP consultant and founder of Practical Linguistics Inc. Her projects include document summarization, information extraction, topic modeling and sentiment analysis of consumer reviews, and document similarity. She is the author of the recently published Python Natural Language Processing Cookbook. Zhenya holds a PhD in Linguistics from the University of California Berkeley and a BS in Computer Science from the Massachusetts Institute of Technology.

Introduction to NLP and Topic Modeling(Workshop)


More Instructors Coming Soon

West 2021 Confirmed Workshops/Training Sessions

Milecia McGregor | Senior Software Engineer | Iterative

Tuning Hyperparameters with Reproducible Experiments 

Ajay Baranwal | Center Director | Center for Deep Learning in Manufacturing

GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow

Jayeeta Putatunda Data Scientist | MediaMath

Build a Question Answering System using DistilBERT in Python

Rongpeng Li | Senior Data Scientist | Galvanize

A Complete Real-Time Data Application in 90 Minutes: from Kafka to Streamlit

Ryan Kasichainula | Sr. Data Science | Galvanize

Sound Classification and Detection with STFT and CNNs

Jennifer Dawn Davis, PhD and Andrea Lowe, PhD | Staff Field Data Scientist and Training & Enablement Engineer | Domino Data Labs

Practical Reinforcement Learning for Data Scientists

Yong Tang, PhD, Director of Engineering

Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline

Victor Lo, PhD | Head of Data Science & Artificial Intelligence | Fidelity Investments

How to Optimize using Uplift Predictive Models and Uplift Prescriptive Analytics

Kai-Wei Chang | Director, Assistant Professor | UCLA NLP, UCLA CS

Transferable Representation Learning in NLP

Hannes Hapke | Machine Learning | Digits

Unstructured Text with Transformers

Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co

MLOps (Machine Learning Operations) From Model to Production

Kumaran Ponnambalam | Big Data, Data Science & Analytics Leader | Cisco

Building a ML Serving Platform at Scale for Natural Language Processing

Sourav Mazumder | Data Scientist, AI & ML Operationalization Leader | IBM

Operationalization of Models Developed and Deployed in Heterogeneous Platforms

Oliver Zeigermann | Software Developer | Embarc Software Consulting GmbH

Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0

Karl Weinmeister | Developer Relations Engineering Manager | Google

Get Started with Time-Series Forecasting using the Google Cloud AI Platform

Jess Garcia | Senior Instructor | SANS Institute

Digital Forensics and Incident Response

More Training Sessions Coming Soon

Machine Learning

  • Meta-learning for Machine Learning

  • Self Supervised learning; new techniques

  • Federated Learning for Data Privacy

  • Explainable AI and Bias in machine learning

  • Machine Learning at Scale using Apache Spark

  • Safety & Robustness in Machine Learning Modeling

  • Semi-supervised learning

  • Causal Inference with Machine Learning

Deep Learning

  • Deep Reinforcement learning

  • Deep Learning with PyTorch & Tensorflow

  • Deep Learning Deep Dive

  • Computer Vision 1/2 Day Training

  • Deep Learning with Keras

  • Introduction to Deep learning

  • Deepfakes Tutorial

  • Graph Representation Learning

NLP

  • Self Supervised learning; new techniques

  • Transfer Learning in NLP

  • Introduction to NLP and Topic Modeling

  • NLP Pre-trained Transformer Models with Bert, Ernie,, and GPT-2

  • State-of-the-Art NLP with PyTorch and Tensorflow

  • Semi-supervised learning

  • Hugging Face Transformer Library Workshop

  • Applications of NLP; Sentiment Analysis, Dialog Systems, and Semantic Search

ADDITIONAL TUTORIALS & WORKSHOPS

  • Machine Learning for Cyber Security

  • Real-time Streaming Analytics

  • MLOps and Machine Learning Pipelines

  • Introduction to Machine Learning Using scikit-learn

  • Auto Machine Learning (AutoML)

  • Distributed Machine Learning

  • Introduction to Data Analysis with Python Pandas

  • Machine Learning Workflow with Kubeflow & Kubernetes

Training & Workshop 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.
Register now | 30% ends soon

Pre-Conference Live Training 
Join one of the October Ai+ Live Training Sessions (a $210) value for free when you purchase an ODSC West Virtual Platinum or In-Person Gold, Platinum, or VIP Pass.

LIVE TRAINING: OCTOBER 6TH

This hands-on course is organized into four lessons. Your learning journey starts with an introduction to NLP, reviewing its evolution over the past 70 years. Then you’ll be introduced two more popular open-source NLP libraries (fast.ai and Hugging Face) and perform state-of-the-art NLP.  Followed by retracing how NLP advanced over the last decade and experienced its breakout moment in 2018. Finally, putting theory to practice and develop your own named entity recognition and text classification models using spaCy.

LIVE TRAINING: OCTOBER 21ST

By the end of this live, hands-on, online course, you’ll understand what MLOps is, how to get started using MLOps and best practices for MLOps. You’ll be able to perform Continuous Integration for Python ML Projects, use the AWS Cloud for MLOps development, create Containerized workflows for MLOps, create Flask and CLI Services for Python ML Projects.

 

LIVE TRAINING: OCTOBER 28TH

You’ll understand in detail how Gradient Boosting models are fit as an ensemble of decision trees and apply that understanding to the feature engineering process, the various parameters of Gradient Boosting and their relative importance and how to appropriately choose them and gain familiarity with the various Gradient Boosting packages and the capabilities, strengths, and weaknesses of each. You will also learn how to interpret, understand, and evaluate a model: both qualitatively and quantitatively.

 

Ai+ is the only hands-on training platform solely developed for AI practitioners. Keep training with the top names in the industry. Free for 6 months with an ODSC West Platinum pass.

Learn More

ODSC Training Includes

Opportunities to form working relationships with some of the world’s top data scientists for follow-up questions and advice.

Access to 40+ training sessions  and 70+ workshops.

Hands-on experience with the latest frameworks and breakthroughs in data science.

Affordable training–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.

Highly Experienced Instructors

Our instructors are highly regarded in data science, coming from both industry and academia.

Real World Applications

Gain the skills and knowledge to use data science in your career and business, without breaking the bank.

Cutting Edge Subject Matter

Find training sessions offered on a wide variety of data science topics, from machine learning to data visualization.

Join ODSC WEST | November 16th - 18th, 2021

Register Now
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