• 01

    ODSC West Prerequisites

    • ODSC West Workshop/Training Prerequisites list

  • 02

    Machine Learning

    • Introduction to Scikit-learn: Machine learning in Python by Thomas Fan

    • Modern Machine Learning in R Part I by Jared Lander

    • Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values by Thomas Fan

    • Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection by Thomas Fan

    • The Life of Scikit-learn: from Tech to People by Gaël Varoquaux, PhD

    • Modern Machine learning in R Part II by Jared Lander

    • Rainforest XPRIZE: Harnessing Data for Good by Peter Houlihan

    • Codeless Reinforcement Learning: Building a Gaming AI by Corey Weisinger

    • Echo State Networks for Time-Series Data by Teal Guidici, PhD

    • Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression by Thomas Fan

    • Probabilistic Programming and Bayesian Inference with Python by Lara Kattan

    • Uplift Modeling Tutorial: From Predictive to Prescriptive Analytics by Victor Lo, PhD

    • Hands-on Reinforcement Learning with Ray RLlib by Paco Nathan

    • Customer2Graph: Powering Customer Analytics with Graph Representations by Srinivas Chilukuri and Kapil Jain

    • Intelligibility Throughout the Machine Learning Life Cycle by Jenn Wortman Vaughan, PhD

    • Prioritize ML Operations at Any Maturity Level by Diego Oppenheimer

    • Beyond OCR: Using Deep Learning to Understand Documents by Eitan Anzenberg, PhD

    • Bayesian Statistics Made Simple by Allen Downey, PhD

    • End to End Modeling & Machine Learning by Jordan Bakerman, PhD and Ari Zitin

    • How AI is Changing the Shopping Experience by Sveta Kostinsky and Marcelo Benedetti

    • Data Science for Suicide Prevention by Jennifer Redmon and Dr. Annie Ying

    • StructureBoost: Gradient Boosting with Categorical Structure by Brian Lucena

    • What Really Matters in Evaluating Machine Learning Models: Swap-Ins / Swap-Outs and How to Use Them by Seth Weidman

    • Advances and Frontiers in Auto AI & Machine Learning by Lisa Amini, PhD

    • Introduction to Generative Modeling Using Quantum Machine Learning by Luis Serrano, PhD and Kaitlin Gili and Alejandro Perdomo, PhD

    • Predicting Model Failures in Production by Aravind Chandramouli, PhD

    • GPU-accelerated Data Science with RAPIDS by John Zedlewski and Corey Nolet

    • Solving Problems with Both Text and Numerical Data Using Gradient Boosting by Stanislav Kirillov

    • Uncertainty Sampling and Diversity Sampling by Robert Munro, PhD

    • A Comparison of Topic Modeling Methods in Python by Russell Martin, PhD

    • Just Machine Learning by Tina Eliassi-Rad, PhD

    • Machine Learning for Biology and Medicine by Sriram Sankararaman, PhD

    • What if We Could Use Machine Learning Models as Database Tables? by Jorge Torres

    • Reinforcement Learning Research with the Dopamine Framework by Pablo Samuel Castro, PhD

    • Building a ML Serving Platform at Scale for Natural Language Processing by Kumaran Ponnambalam

    • The Bayesians are Coming! The Bayesians are Coming, to Time Series by Aric LaBarr, PhD

    • Interpretable Machine Learning with Python by Serg Masis

    • Building ML Models in a Cloud Environment by Bill Wright,Martin Isaksson and Robert Lundberg

    • The Fundamentals of Statistical Time Series Forecasting by Jeffrey Yau, PhD

    • Maximizing Dataset Potential: Challenges, Considerations & Best Practices by Soo Yang

  • 03

    MLOps & Management

    • Rapid Data Exploration and Analysis with Apache Drill by Charles Givre

    • MLOps in DL Model Development by Anna Petrovicheva

    • Framework for Model Monitoring at Scale by Josh Poduska and Dr. James Pearce

    • End-to-end AI Application Development with Programmatic Supervision by Alex Ratner, PhD

    • Build an ML pipeline for BERT models with TensorFlow Extended – An end-to-end Tutorial by Hannes Hapke

    • Data Science: How Do We Achieve the Most Good and Least Harm? by Megan Price, PhD

    • Model Governance: A Checklist for Getting AI Safely to Production by David Talby, PhD

    • Lessons from KPI Monitoring and Diagnosis at Scale by Peter Bailis, PhD

    • Unify Analytics – Combine Strengths of Data Lake and Data Warehouse by Paige Roberts

  • 04

    Deep Learning

    • Keras from Soup to Nuts – An Example Driven Tutorial by Sujit Pal

    • Modern and Old Reinforcement Learning Part 1 by Leonardo De Marchi

    • AlphaStar: Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning by Oriol Vinyals, PhD

    • Natural Language Processing with PyTorch by Yashesh A. Shroff, PhD and Ravi Ilango

    • Modern and Old Reinforcement Learning Part 2 by Leonardo De Marchi

    • Deep Learning (with TensorFlow 2) by Dr. Jon Krohn

    • Conversational AI with DeepPavlov by Mikhail Burtsev, PhD and Daniel Kornev

    • Learning with Limited Labels by Shanghang Zhang, PhD

    • Interacting with Deep Generative Models for Content Creation by Bolei Zhou, PhD

    • State of the art AI Methods with TensorFlow: Transfer Learning, RL and GANs by Daniel Whitenack, PhD

    • Ludwig, a Code-Free Deep Learning Toolbox by Piero Molino, PhD

    • Building Content Embedding with Self Supervised Learning by Sijun He and Kenny Leung

    • Continuous-time Deep Models for Forecasting Sparse Time Series by David Duvenaud, PhD

    • A Hands-On Tutorial for Training Interpretable Variational Autoencoders Using siVAE by Gerald Quon, PhD and Yongin Choi

    • Testing Production Machine Learning Systems by Josh Tobin, PhD

    • Applied Deep Learning: Building a Chess Object Detection Model with TensorFlow by Joseph Nelson

    • Learning Intended Reward Functions: Extracting all the Right Information from All the Right Places by Anca Dragan, PhD

  • 05

    Research Frontiers

    • The Era of Brain Observatories: Open-Source Tools for Data-Driven Neuroscience by Ariel Rokem, PhD

    • Making Deep Learning Efficient by Kurt Keutzer, PhD

  • 06

    R Programming

    • Introduction to Shiny Application Development by Bethany Poulin

    • Fast Data Access in R and Python with Apache Arrow by Neal Richardson,PhD

    • Taking Unique Advantage of High Missing Data Scenarios by Anne Lifton

  • 07

    Applied AI

    • How to Stop Worrying and Start Tackling AI Bias by Jett Oristaglio

    • Some Failures and Lessons Learned Using AI in our AI Company by Dustin Burke and Borys Drozhak

    • The Rise of MLOps by Seph Mard

    • Realizing Value through DataRobot’s AI-Powered Apps by Ina Ko

    • Lessons Learned with Data & Storytelling by Danny Ma, Kate Strachnyi, Jen Underwood, Susan Walsh, Ben Taylor, PhD

    • Experimentation, Metrics and Analytics: An Ecosystem for Data Informed Decisions by Eric Weber

    • Fireside Chat with Jacqueline Ros Amable - AI in Climate Tech by Ryan Sevey and Jacqueline Amable

    • Components of AI Infrastructure & MLOps by Michael Balint

    • Building an Analytics COE: One Leader's Story by Edward M. Young

    • Solving Practical Computer Vision Problems in 10 Minutes by Anton Kasyanov and Ivan Pyzow

    • Hands-on Data Science for Software Developers -- A Live Coding Session with Data Robot Self-Service by David Gonzalez

    • A Tutorial on Robust Machine Learning Deployment by Tim Whittaker and Rajiv Shah, PhD

    • The AI Practitioner Series - Data Prep Walkthrough (A Reusable Framework!) by Sean Smith and Shyam Ayyar

  • 08

    AI for Good

    • Communicating COVID: Visualization, Models, and Uncertainty during a Pandemic by Jonathan Industries

    • Semantic Scholar and the Fight Against COVID-19 by Oren Etzioni, PhD

    • Bayesian Workflow as Demonstrated with a Coronavirus Example by Andrew Gelman, PhD

    • What to Expect When You Are Expecting Robots - The Future of Human-Robot Collaboration by Julie A. Shah, PhD

    • Creating Equality and Inclusivity with Feature Engineering by Vida Williams

    • Using Artificial Intelligence to Save Lives at Birth by Charles Onu, PhD

    • The State of Serverless and Applications to AI by Joe Hellerstein, PhD

    • Diversity in Data Science: Challenges and Possibilities  by Marie desJardins, PhD

  • 09

    Data Science Kick-Starter

    • Getting Started with Pandas for Data Analysis by Boris Paskhaver

    • ML Easel – Tredence’s Data Science and ML Engineering Workbench by Changa Reddy

  • 10

    Data Visualization

    • Painting with Data: Introduction to d3.js by Ian Johnson

    • Data Visualization: From Jupyter to Dashboards by David Yerrington

    • Exploring the Interconnected World: Network/Graph Analysis in Python by Noemi Derzsy, PhD

    • Best Practices for Optimizing Migration to the Cloud by Ernie Ostic

  • 11

    NLP

    • State-of-the-Art Natural Language Processing with Spark NLP by David Talby, PhD

    • Evaluating and Testing Natural Language Processing Models by Sameer Singh, PhD

    • Topic-Adjusted Visibility Metric for Scientific Articles by Tian Zheng, PhD

    • Language Complexity and Volatility in Financial Markets: Using NLP to Further our Understanding of Information Processing by Ahmet K. Karagozoglu, Ph.D.

    • Deep Learning-Driven Text Summarization & Explainability by Nadja Herger, PhD and Nina Hristozova Viktoriia Samatova

    • Natural Language Processing: Feature Engineering in the Context of Stock Investing by Frank Zhao

    • Remote HPCC Systems/ECL Training by Bob Foreman and Hugo Watanuki

    • Training Conversational Agents on Noisy Data by Phoebe Liu

    • Transfer Learning in NLP by Joan Xiao, PhD

    • Accelerating NLP Model Training and Deployment with PyTorch by Prasanth Pulavarthi

  • 12

    Keynotes

    • Are We Ready for the Era of Analytics Heterogeneity? Maybe… but the Data Says No by Marinela Profi

    • Health AI: What's Possible Now and What's Hard by Suchi Saria, PhD

    • A Secure Collaborative Learning Platform by Raluca Ada Popa, PhD

    • Data for Good: Ensuring the Responsible Use of Data to Benefit Society by Jeannette M. Wing, PhD

    • Our Applied AI Future by Ben Taylor, PhD

    • Applying AI to Real World Use Cases by John Montgomery

    • Generalized Deep Reinforcement Learning for Solving Combinatorial Optimization Problems by Azalia Mirhoseini, PhD

    • Frontiers of Probabilistic Machine Learning by Zoubin Ghahramani, PhD

    • The Future of Computing is Distributed by Ion Stoica, PhD

  • 13

    Business Talks

    • Overcoming Obstacles to AI Execution: Trust, Scale, and Reasoning by Mark Weber

    • Going Beyond FAIR to Create a Connected Data Ecosystem by Susan Gregurick, PhD

    • A Human-Machine Collaboration Built on Trust and Accountability by Dr. Biplav Srivastava

    • Business Skills for Data Scientists by Liz Sander, PhD

    • How Google Uses AI and Machine Learning in the Enterprise by Rich Dutton

    • Strategies for Building AI-ready Data Sources and (Semi)autonomous Reasoning Agents Operating on Top of Them by Marcin von Grotthuss, PhD

    • Inverse Reinforcement Learning for Financial Applications by Igor Halperin, PhD

    • Solving Real-life Challenges in Detecting Cognitive Diseases from Speech using ML by Jekaterina Novikova, PhD

    • Jupyter as an Enterprise "Do It Yourself" (DIY) Analytic Platform by Dave Stuart

    • Tackling Ethical Risk and Bias in Machine Learning Applications by Javed Ahmed, PhD

  • 14

    Demo Talks

    • What if AI Could Craft the Next Generation of your AI? by Yonatan Geifman, PhD

    • A Quick, Practical Overview of KNIME Analytics Platform by Paolo Tamagnini

    • Personalize.AI: Transforming Businesses Through Personalization by Gopi Vikranth and Dr. Prakash

    • Leverage Data Lineage to Maximize the Benefits of AI and Big Data by Ernie Ostic

    • Integrating Open Source Modeling with SAS Model Manager by Scott Lindauer, PhD and Diana Shaw

    • Improving Your Data Visualization Flow with Altair and Vega-Lite by Rachel House

    • An Overview of Algorithmia: How to Deploy, Manage, and Scale Your Machine Learning Model Portfolio by Kristopher Overholt

    • DataRobot Enterprise AI Platform: End-to-End Demonstration by Andy Lofgreen

    • Responsible AI with Azure Machine Learning by Mehrnoosh Sameki, PhD

    • Accelerate Time-to-Model by Simplifying the Complexity of Feature Engineering by Daniel B Gray and John Lynch

    • [Deep Learning] Fresh Data in Days Instead of Months by Anthony Sarkis

    • Supercharge your Training Data Quality with Samasource by Abha Laddha

    • Budgeting, Building & Scaling Data Labeling Operations by Soo Yang

    • Implementing an Automated X-Ray Images Data Pipelines, the Cloud-native Way! by Guillaume Moutier

    • DataOps: The Secret Advantage for ML and AI Success by Cody Rich

    • Automated Model Management with ML Works by Pavan Nanjundaiah

    • Next-Generation Big Data Pipelines with Prefect and Dask by Aaron Richter, PhD

    • How to Increase ML Server Utilization With MLOps Visualization Dashboards by Yochay Ettun

    • HPCC Systems – The Kit and Kaboodle for Big Data and Data Science by Bob Foreman and Hugo Watanuki

    • Jumpstart Your Data Science Career with The Data Incubator by Sierra King

    • Meet the New Hot Analytics Stack - Apache Kafka, Spark and Druid by Danny Leybzon

    • Centralizing Data Science Work and Infrastructure Access Across the Enterprise by Ross Sharp

  • 15

    Career Mentor Talks

    • Mental Models for Building Your Career in Data Science by Chirasmita Mallick

    • A Data Scientist from Academia to Industry: things you should know! by Wjdan Alharthi

    • The Data Engineering Path by Daniela Petruzalek

    • How Data Scientists Can Support Their Organization's DEI Efforts by Timi Dayo-Kayode

    • Am I Ready for a Data Science Job? by Aadil Hussaini

    • Coding Challenges: What Are hiring Companies Looking For? by Arwen Griffioen, PhD

    • Data Science Success Stories by Jeff Anderson

  • 16

    Extras

    • ODSC Ignite: Women in Data Science

    • AI Investors Reverse Pitch

    • Learning from Failure - Incredible Stories from Successful Business Leaders