May 9th-11th, 2023

Data Engineering & MLOps

Focusing on the practice, engineering, workflows and DataOps of data science
First 100 Tickets | Register now

Track Sponsored By:

As data science extends its reach across an enterprise, the need for better management, workflow, production and deployment practices increases. The challenges of deploying and monitoring models in production, managing data science workflows and teams, and understanding ROI are a few of the issues organizations wrestle with.

Learn best practices for effective data science management

Sessions in this broad focus area will look at uses cases, best practices, and stories from the field to show how to effectively incorporate data science practice into the wider business process.  This focus area will look beyond data sourcing and modeling towards the many challenges teams need to overcome to effectively apply data science in their organization.

Some of Our Past Data Engineering & MLOps Speakers

Co-Founder and CTOIguazio

Yaron Haviv

Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases. He also initiated and built Nuclio, a leading open source serverless platform with over 4,000 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. He was also the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.

From AutoML to AutoMLOps(Track Keynote)

 

Director Of EngineeringMobileIron

Yong Tang, PhD

Yong Tang, Ph.D., is Director of Engineering at Ivanti. He is a core contributor of many open-source projects in machine learning and cloud native areas. He is a maintainer and SIG I/O lead of the TensorFlow project, and received the Open Source Peer Bonus award from Google for his contributions to TensorFlow. He is also a maintainer of Docker/Moby, the widely used open-source container platform, and a core maintainer of CoreDNS, a Cloud Native Computing Foundation (CNCF) graduated project for service discovery.

Tutorial: Building and Deploying Machine Learning Models with TensorFlow and Keras(Training)

 

Principal ML Solutions ArchitectDatabricks

Sean Owen

Sean is a principal solutions architect focusing on machine learning and data science at Databricks. He is an Apache Spark committer and PMC member, and co-author Advanced Analytics with Spark. Previously, he was director of Data Science at Cloudera and an engineer at Google.

MLOps: Relieving Technical Debt in ML with MLflow, Delta and Databricks(Talk)

Co-founder & CEOOuterbounds

Ville Tuulos

Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of an upcoming book, Effective Data Science Infrastructure, published by Manning.

Human-Friendly, Production-Ready Data Science with Metaflow(Talk)

Senior Data Scientist Databricks

Yinxi Zhang, PhD

Yinxi Zhang is a Sr Data Scientist at Databricks with 7+ years of industry experience on end to end ML development. Her responsibilities as a Brickster are teaching Scalable Machine Learning, Deep Learning and MLops courses and helping clients develop their ML solutions. She used to be a marathon runner and now a yogi.

MLOps: Relieving Technical Debt in ML with MLflow, Delta and Databricks(Talk)

VP of ML | Adjunct Professor | ChairArthurAI | Georgetown | CAMLIS

Keegan Hines, PhD

Keegan is VP of Machine Learning at ArthurAI and is also an Adjunct Assistant Professor at Georgetown University. Previously, he was the Director of Machine Learning Research at Capital One and has also held roles at cyberdefense firms. He is a Co-Founder of the Conference on Applied Learning for Information Security (CAMLIS) and holds a PhD in Neuroscience from the University of Texas.

Drift Detection in Structured and Unstructured Data(Talk)

Head of Developer RelationSeldon

Ed Shee

Ed Shee, Head of Developer Relations at Seldon. Having previously led a tech team at IBM, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.

An Introduction to Drift Detection(Workshop)

Managing PartnerAboriginal Armadillo

Trevor Grant

Trevor is the Director of Developer Relations at Arrikto and an international speaker excited to be back on the road after a 2 year COVID hiatus. He is also a member and involved with leadership of several projects at the Apache Software Foundation, PMC Chair of Apache Mahout, and Author of Kubeflow For Machine Learning: From Lab to Production.

Tower of Babel: Making Apache Spark, Apache Mahout, Kubeflow, and Kubernetes Play Nice(Talk)

Founding Engineer | Co-chairAkuity | Kubeflow

Yuan Tang

Yuan is currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platform. He’s a PMC member of XGBoost and Apache MXNet, co-chair of Kubeflow, maintainer of TensorFlow, Argo, Couler, and ElasticDL, as well as author of numerous open source projects. He’s the author of Distributed Machine Learning Patterns (https://github.com/terrytangyuan/distributed-ml-patterns) as well as the co-author of TensorFlow in Practice and Dive into Deep Learning (with TensorFlow).

Data Science in the Cloud-Native Era(Talk)

MLOps ArchitectWhyLabs

Danny D. Leybzon

Danny D. Leybzon has worn many hats, all of them related to data. He studied computational statistics at UCLA and has worked in the data and ML space ever since. In his role as MLOps architect, he has worked to evangelize machine learning best practices, talking on subjects such as distributed deep learning, productionizing machine learning models, automated machine learning, and lately has been talking about AI observability and data logging. When Danny’s not researching, practicing, or talking about data science, he’s usually doing one of his numerous outside hobbies: rock climbing, backcountry backpacking, skiing, etc.

Achieving Better Models Through Monitoring(Demo Talk)

 

Technical EvangelistPaperspace

James Skelton

James Skelton is a technical evangelist who specializes in Machine Learning. After graduating from the University of St. Andrews and completing Galvanize’s Data Science Immersive program, James has been focused on creating accessible educational content, community building, and identifying meaningful trends surrounding the worlds of Deep Learning and AI development.

What We’ve Learned Pushing Nearly 100M Hours of GPU Compute(Talk)

ML Product ResearcherSeMI Technologies

Laura Ham

Laura is a ML Product Researcher at SeMI Technologies, the company behind the open-source vector search engine Weaviate. She researches new machine learning features for Weaviate and works on everything UX/DX related to Weaviate. For example, she is responsible for the GraphQL API design. She is in close contact with our open source community. Additionally, she likes to solve custom use cases with Weaviate, and introduces Weaviate to other people by means of Meetups, talks and presentations.

Vector Search – A Gentle Introduction(Talk)

Principal Data ScientistOracle

John Peach

A modern polymath, John holds advanced degrees in mechanical engineering, kinesiology and data science, with a focus on solving novel and ambiguous problems. As a senior applied data scientist at Amazon, John worked closely with engineering to create machine learning models to arbitrate chatbot skills, entity resolution, search, and personalization.

As a principal data scientist for Oracle Cloud Infrastructure, he is now defining tooling for data science at scale. John frequently gives talks on best practices and reproducible research. To that end, he has developed an approach to improve validation and reliability by using data unit tests and has pioneered Data Science Design Thinking. He also coordinates SoCal RUG, the largest R meetup group in Southern California.

Tired of Cleaning your Data? Have Confidence in Data with Feature Types(Workshop)

Co-founder and CEO Metaplane

Kevin Hu

Kevin Hu is co-founder and CEO of Metaplane, a data observability company based in Boston focused on helping every team find and fix data quality problems with as little setup as possible. Metaplane is backed by leading investors including Y Combinator and the founders of Okta, HubSpot, and Lookout, and is used across high-growth teams and large enterprises.

Kevin has over a decade of experience working in data. Most recently, he researched the intersection of machine learning and data science at MIT, where he collaborated with Fortune 500 companies while earning his PhD, SM, and SB. His research has been published in top computer science venues like ACM CHI, KDD, and SIGMOD, and featured in the New York Times, Wired, and The Economist.

The Origins, Purpose, and Practice of Data Observability(Talk)

Data Observability in 10 Minutes(Demo Talk)

Data Science AdvocateNeo4j

Dr. Clair J. Sullivan

Dr. Clair Sullivan is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she began her career in nuclear emergency response at Los Alamos National Laboratory where her research involved signal processing of spectroscopic data. She spent 4 years working in the federal government on related subjects and returned to academic research in 2012 as an assistant professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign. While there, her research focused on using machine learning to analyze the data from large sensor networks. Deciding to focus more on machine learning, she accepted a job at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. In 2021 she joined Neo4j as a Graph Data Science Advocate. Additionally, she founded a company, La Neige Analytics, whose purpose is to provide data science expertise to the ski industry. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers. Dr. Sullivan was the recipient of the DARPA Young Faculty Award in 2014 and the American Nuclear Society’s Mary J. Oestmann Professional Women’s Achievement Award in 2015.

When SQL is Not the Best Answer: Identifying “Graph-y” Problems and When Graphs Can Help(Talk)

Founder, CEO & Chief ScientistMerly, Inc.

Justin Gottschlich, Ph.D.

Justin Gottschlich is the Founder, CEO & Chief Scientist of Merly, Inc. (http://merly.ai), a company aimed at making software developers more productive using state-of-the-art machine programming systems. Justin also has an academic appointment as an Adjunct Assistant Professor at the University of Pennsylvania. Before founding Merly, Justin was a Principal AI Scientist and the Founder & Director of Machine Programming Research at Intel Labs. In 2017, he co-founded the ACM SIGPLAN Machine Programming Symposium (MAPS) and now serves as its Steering Committee Chair. Justin also serves on the 2020 NSF Expeditions advisory board “Understanding the World Through Code” led by MIT Prof. Armando Solar-Lezama. Justin received his PhD in Computer Engineering from the University of Colorado-Boulder in 2011 and has 40+ peer-reviewed publications, 50+ issued patents, with 100+ patents pending. Justin’s research has been highlighted in venues like The New York Times, Communications of the ACM, MIT Technology Review, and The Wall Street Journal.

The Future of Software Development Using Machine Programming(Tutorial)

Data Science LeadPayPal

Gulrez Khan

Gulrez Khan is a Lead Data Scientist at PayPal and has been wrangling with data for more than 14 years. He champions Data Visualization & Storytelling with data in the community and has been giving talks, speaking at conferences to democratize data. Outside his day job, he loves to play with the public dataset and is also teaching kids to draw data.

Telling Stories with Data(Tutorial)

Senior Data ScientistRue Gilt Groupe

Ronny Mathew

Ronny Mathew is a Data Science lead at Rue Gilt Groupe building next-generation online shopping experiences for their members. He is passionate about applied machine learning and deep learning and works on recommendation systems, computer vision, and Natural language processing for big data. At RGG, they are currently building the next generation of their personalization platform leveraging cutting-edge tools and algorithms.

Quick to Production with the Best of Both Spark and Tensorflow(Talk)

Founder and CEOthatDot

Ryan Wright

Ryan Wright is the creator of Quine, and has been leading software teams focused on data infrastructure and data science for two decades. He has served as principal engineer, director of engineering, principal investigator on DARPA-funded research programs, and is currently the founder and CEO of thatDot—the company supporting Quine. Ryan particularly enjoys taking the philosophical ends of computer science—usually problems related to language, meaning, and data—and making them more practical.

Noiseless Anomaly Detection with Streaming Graph A.I.(Demo Talk)

Quine: A Streaming Graph for Event-Driven Data Pipelines(Talk)

Head of Data Science Evangelism and MarketingOuterbounds

Hugo Bowne-Anderson, PhD

Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.

Full-stack Machine Learning for Data Scientists(Tutorial)

 

Product Marketing ManagerSAS

Marinela Profi

As a Marketing Manager for data science and open-source, Marinela uses her cross-domain expertise in statistics, business and marketing, to position SAS as a leader in the Data Science and Machine Learning Platform market. She focuses on helping customers apply advanced analytics, machine learning, natural language processing and forecasting to solve their most complex problems. Over the past 5 years, Profi honed her skills mining data, developing models and technical/business solutions, including deploying AI at scale. Her experience spans banking, manufacturing, retails and energy. She is a keynote speaker and presenter at different global conferences, where she shares trend and priorities of the data science industry. She is a published author, contributor to several eBooks, and blog writer on major industry and data science blogs. She has a bachelor’s in economics, an MBA and a master’s in statistics. She is passionate about getting more younger passionate to code and pursue careers in STEM.

Developing, Deploying and Managing Models at Scale with SAS® Viya®(Talk)

 

Professor | Founding Director | Co-founder and CTOStatistics and Data Science, MIT | IkigaiLabs

Devavrat Shah, PhD

Devavrat Shah is Andrew (1956) and Erna Viterbi Professor with the department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology. He is the founding director of Statistics and Data Science at MIT. He is also a member of IDSS, LIDS, CSAIL and ORC at MIT. He co-founded Celect, Inc. (now part of Nike) in 2013 to help retailers decide what to put where by accurately predicting demand using omni-channel data. He is a co-founder and CTO of IkigaiLabs with the mission to build self-driving organizations by enabling data-driven operations with human-in-the-loop. His research focuses on statistical inference and stochastic networks. His contributions span a variety of areas including resource allocation in communications networks, inference and learning on graphical models, algorithms for social data processing including ranking, recommendations and crowdsourcing and more recently causal inference. He has made foundational contributions to the development of “gossip” protocols and “message-passing” algorithms for statistical inference which have been the building blocks of modern distributed data processing systems.His work spans a range of areas across electrical engineering, computer science and operations research. His work has received broad recognition, including prize paper awards in Machine Learning, Operations Research and Computer Science, and career prizes including 2010 Erlang prize from the INFORMS Applied Probability Society, awarded bi-annually to a young researcher who has made outstanding contributions to applied probability. He is a distinguished alumni of his alma mater IIT Bombay from where he graduated with the honor of President of India Gold Medal. His work has been covered in popular press including NY Times, Forbes, Wired and Reditt.

Automation for Data Professionals(Training)

Resarch EngineerSeldon

Ashley Scillitoe

Ashley is a data science research engineer at Seldon, where he works on developing production-ready tools for drift, adversarial and outlier detection. Prior to joining Seldon, he spent a number of years as a Research Fellow at The Alan Turing Institute. Here, he explored the use of machine learning for tackling aerospace engineering problems, with a focus on explainability and uncertainty quantification. Ashley also completed a PhD at the University of Cambridge, and is a keen proponent of open-source software.

An Introduction to Drift Detection(Workshop)

Senior Knowledge EngineerBeamery

Henri Egle Sorotos

Henri is Senior Knowledge Engineering at Beamery – a full-talent lifecycle scaleup making sense of enterprise people data, based in London and the US. At work, he specialises in ontology design, entity reconciliation and data provenance for semantic web databases. Henri has worked at a number of HRTech startups, and is passionate about modelling and serving AI models in the domain of people, skills, companies and occupations.

A Global Knowledge Graph of People, Skills And Companies: How Ontology Design is Key to Enabling AI Solutions in HR(Workshop)

Director of Sales EngineeringIguazio

Marcelo Litovsky

Marcelo Litovsky is an experienced Information Technology professional with 30 years of diverse background in Enterprise Architecture, AI, Systems and Database Management, and Programming. He has worked in multiple industries: Financial Services, Entertainment, and Information Technology in his career. Today, he serves as Director of Sales Engineering at Iguazio, bringing his expertise to help Data Scientists, Data Engineers, and Systems Engineers work together to deploy AI/ML applications faster, more efficiently and in a reproducible way. When he is not installing software, talking to customers, or writing Python code, you can find him at the gym or preparing healthy vegan meals.

“It worked on my laptop, now what?” Using OS Tool MLRun to Automate the Path to Production(Demo Talk)

CEO and FounderCoiled

Matthew Rocklin, PhD

Matthew Rocklin, CEO and founder of Coiled, and the initial author of Coiled’s underlying technology, Dask. He developed Dask to help people solve challenging distributed computing problems while working at Anaconda. While he is primarily known for his work on Dask, he also coordinates and maintains several dozen libraries within Python’s numeric computing ecosystem, with a substantial focus on efficient and scalable computing. Matthew is a frequent speaker at several technical, academic, and industry events, such as PyData, SciPy, Google Next, O’Reilly’s Strata, AGU, AMS, and ICML. He has a Doctorate of Philosophy in Computer Science from the University of Chicago, and a Bachelors in Physics, Mathematics, and Astronomy from the University of California.

What I Love and Hate About Dask(Talk)

M.S. Data Science at Harvard University, Z by HP Global Data Science Ambassador

Max Urbany

As Max progresses through his Master’s Program, he is particularly interested in intelligent digital accessibility design, along with the ethical analysis of existing predictive models. His passion for creating quality user-centered tools drives him to understand as much as he can about end users while leveraging what data can reveal.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

VP, Enterprise AI / Data Science Solutions at Future Tech

Dan Chaney

Dan Chaney is the VP, Enterprise AI / Data Science Solutions, for Future Tech Enterprise, Inc., an award-winning global IT solutions provider. He oversees all sales, marketing, and technical activities focused on Future Tech’s comprehensive range of AI and data science workstation solutions. Prior to joining Future Tech, Dan spent 20 years at Northrop Grumman, most recently serving as the company’s Enterprise Director of IT Solution Architecture & Engineering. Dan earned his bachelor’s and master’s degrees in communication and computer science from the University of Kentucky. Dan is a Certified Information Systems Security Professional (CISSP) and adjunct instructor for the University of Louisville’s cybersecurity workforce program sponsored by the National Centers of Academic Excellence in Cybersecurity.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

North America Business Development Manager, Z by HP

Kristin Hempstead

Kristin has been with HP for 11 years and is currently the North America business development manager for HP’s data science and artificial intelligence solutions focusing on federal, education, and public sector customers. She has an MBA from University in South Florida with a specialization in Finance and MIS and a BS in Agriculture from the University of Georgia.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

Field Solution Evangelist – AI and AnalyticsPure Storage

Miroslav Klivansky

Miroslav is a Field Solution Evangelist focused on AI and Analytics. He has extensive experience in storage systems, workload modeling, system architecture, tuning, and benchmarking. Miroslav loves both learning and teaching, and has translated those passions into helping organizations get value from innovative technologies. He has previously worked with top companies (including Facebook, NetApp, and Sun) and a number of startups, and is proud to share his experience. Much of Miroslav’s career has revolved around technical evangelism and performance — focused on system architecture and storage, and blending time with customers with time in the lab. He’s been at this for over 30 years now, with his first paid computer gig involving DOS batch scripts the summer before college.

Simplifying MLOps by Taking Storage Worries out of the Equation(Talk)

Chief Scientist of AIPachyderm

Jimmy Whitaker

Jimmy Whitaker is the Chief Scientist of AI at Pachyderm. He focuses on creating a great data science experience and sharing best practices for how to use Pachyderm. When he isn’t at work, he’s either playing music or trying to learn something new, because “You suddenly understand something you’ve understood all your life, but in a new way.”

Rethinking ML Development – A Data-Centric Approach(Talk)

Senior Data ScientistFINRA

Alex Eftimiades

Alex is a data scientist at FINRA. He applies machine learning and statistics to identify anomalous and suspicious trading and has helped to develop model validation procedures and tools. Alex originally studied physics and is passionate about applying math to solve real world problems. He previously worked as a data engineer and as a software engineer.

Introducing Model Validation Toolkit(Talk)

Software Development Engineer In TestFINRA

Matt Gillett

Matthew Gillett is an Associate Director at FINRA who manages a team of Software Development Engineers in Test (SDET) across multiple projects. In addition to his primary focus in software development and assurance engineering, he also has an interest in various other technology topics such as big data processing, machine learning, and blockchain.

Introducing Model Validation Toolkit(Talk)

 

Senior Solutions Architect & Apache Flink CommitterVerverica

Seth Wiesman

Bio Coming Soon!

Changelog Stream Processing with Apache Flink(Tutorial)

Sales EngineerRun:Ai

Robert Magno

Rob Magno is a Sales Engineer/Solution Architect at Run:AI based in New Jersey. He has been working in the Docker and Kubernetes space for the past five years. He enjoys tackling the diverse customer challenges that come with orchestrating AI/ML workloads through Kubernetes.

Building the Best AI Infrastructure Stack to Accelerate Your Data Science(Demo Talk)

Data ScientistWhyLabs

Felipe de Pontes

Felipe is a Data Scientist in WhyLabs. He is a core contributor to whylogs, an open-source data logging library, and focuses on writing technical content and expanding the whylogs library in order to make AI more accessible, robust, and responsible. Previously, Felipe was an AI Researcher at WEG, where he researched and deployed Natural Language Processing approaches to extract knowledge from textual information about electric machinery. He is also a Master in Electronic Systems Engineering from UFSC (Universidade Federal de Santa Catarina), with research focused on developing and deploying fault detection strategies based on machine learning for unmanned underwater vehicles. Felipe has published a series of blog articles about MLOps, Monitoring, and Natural Language Processing in publications such as Towards Data Science, Analytics Vidhya, and Google Cloud Community.

Visually Inspecting Data Profiles for Data Distribution Shifts(Workshop)

Data and Machine Learning Scientist

Hadrien Jean, PhD

Hadrien Jean is a machine learning scientist working at My Medical Assistent where he is developing deep learning models in the medical domain. He wrote the book Essential Math for Data Science (https://www.essentialmathfordatascience.com/) aimed at helping people to get the math needed in data science from a coding perspective. He previously worked at Ava on speech diarization. He also worked on a bird detection project using deep learning. He completed his Ph.D. in cognitive science at the École Normale Supérieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https://hadrienj.github.io/posts/).

Introduction to Linear Algebra for Data Science and Machine Learning With Python(Bootcamp)

 


More talks, hands-on workshop and training sessions
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Who Should Attend

Data Science is cross industry and cross enterprise, impacting many different departments across job roles and functions. This track is not only for data scientists of all levels but for anyone interested in the practice and management of data science, including:

  • Data scientists moving beyond model experimentation looking to understand production workflow

  • Data scientists seeking to improve the overall practice of management and development

  • Anyone interested in understanding better collaborative and agile management techniques as applied to data science

  • Business professionals and industry experts looking to understand data science in practice

  • Software engineers and technologists who need to work with data science workflows and understand the unique requirements of these systems

  • CTO, CDS, and other managerial roles that require a bigger picture view of data science

  • Technologists in the field of DevOps, databases, project management and others looking to break into data science

  • Students and academics looking for more practical applied training in data science tools and techniques

Why Attend?

Accelerate and broaden your knowledge of key areas in data science, including deep learning, machine learning, and predictive analytics

With numerous introductory level workshops, you get hands-on experience to quickly build your skills

Post-conference, get access to recorded talks online  and learn from over 100+ high quality recording sessions that let you review content at your own pace 

Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science practice and management

Learn directly from world-class instructors who are the authors and contributors to many of the tools and languages used in data science today

Meet hiring companies, ranging from hot startups to Fortune 500, looking to hire professionals with data science skills at all levels

Network at our numerous lunches and events to meet with data scientists, enthusiasts, and business professionals

Get access to other focus area content, including machine learning & deep learning, data visualization, and much more

What You’ll Learn

Data science has many focus areas.  The goal of this track is to accelerate your knowledge of data science through a series of introductory level training sessions, talks, tutorials and workshops on the most important data science tools and topics.  

  • Experimentation to Production

  • Data Science DevOps

  • Agile Data Science

  • Data Science Architecture

  • Runtime Pipelines

  • Model Monitoring & Auditing

  • Model Depreciation in Production

  • Manage Data Science in Your Organization

  • Collaborative Practices and Tools

  • Team Management

  • Data Science Workflows

  • Data Provenance & Governance

  • Best Practices & Uses Cases

  • Cross Industry & Cross Enterprise Challenges

ODSC EAST 2023 | May 9th-11th

First 100 Tickets | Register now

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