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.
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Meet Some of our 2021 Instructors

Director of Data Analytics and AI SolutionsGoogle Cloud

Valliappa Lakshmanan

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

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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 developer 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.

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.

Risk Management SpecialistFederal Reserve Bank of Chicago

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.

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.

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.

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.

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”.


West 2021 Confirmed 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

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

Yong Tang, PhD, Director of Engineering

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

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

MLOps (Machine Learning Operations) From Model to Production

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

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

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.

Sign Up for ODSC WEST | November 16th - 18th, 2021

Register Now
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