260 speakers & 300 hours of content

The leading conference for Data Science using the latest tools, languages and frameworks.

On-Demand Recordings

  • 1

    ODSC East Keynotes

    • The Big Wave of AI at Scale by Luis Vargas, PhD

    • Is Your ML Secure? Cybersecurity and Threats in the ML World by Dr Hari Bhaskar, PhD and Jean-Rene Gauthier, PhD

    • Bridging the Gap Between Data Scientists and Decision Makers by Ken Jee

    • Accelerate AI/ML Deployments with Enterprise-grade MLOps by Matt Akins, Abhinav Joshi

    • Data Science and AI in Digital Transformation: Digital Can Lead to Blindness by Usama Fayyad, PhD

  • 2

    ODSC Talks

    • MLOps in the Enterprise by Abe Omorogbe

    • A bamboo of Pandas: crossing Pandas' single-machine barrier with Apache Spark by Itai Yaffe Daniel Haviv

    • The Power Of Hexagons: How H3 & Foursquare Are Transforming Spatial Analytics by Nick Rabinowitz

    • What I love and hate about Dask by Matthew Rocklin, PhD

    • Responsible AI for Customer Product Organizations by Aishwarya Srinivasan

    • Need of Adaptive Ethical ML Models in Post Pandemic Era by Sharmistha Chatterjee and Juhi Pandey

    • MLOps: Relieving Technical Debt in ML with MLflow, Delta and Databricks by Sean Owen Yinxi Zhang, PhD

    • What do lawyers need (and want) from Legaltech? by Dr Felicity Bell

    • Security Operations for Machine Learning at Scale with MLSecOps by Alejandro Saucedo

    • Emotion Detection with Natural Language Inference by Serdar Cellat, PhD

    • Open-source Best Practices in Responsible AI by Violeta Misheva, PhD Daniel Vale

    • Human-Friendly, Production-Ready Data Science with Metaflow by Ville Tuulos

    • Drift Detection in Structured and Unstructured Data by Keegan Hines, PhD

    • Learned Optimizers: Learning to Learn Optimization Algorithms by Luke Metz

    • Unsolved ML Safety Problems Dan Hendrycks

    • Can We Let AI be Great? Practical Considerations in Designing Effective and Ethical AI Products. by Masheika Allgood

    • Timing IoT Devices to Slash Carbon Emissions at Scale by Gavin McCormick

    • Trustworthy AI by Jeannette M. Wing, PhD

    • WeightWatcher, an Open-Source Diagnostic Tool for Analyzing Deep Neural Nets by Michael Mahoney, PhD

    • The Origins, Purpose, and Practice of Data Observability by Kevin Hu

    • Best Practices for Data Annotation at Scale by Jai Natarajan

    • Tower of Babel: Making Apache Spark, Apache Mahout, Kubeflow, and Kubernetes Play Nice by Trevor Grant

    • Methods and Tools for Time Series Data Science Problems with InfluxDB, an Open-Source Time Series Database by Anais Dotis-Georgiou

    • How to Supercharge Spark with Apache Iceberg by Ryan Blue

    • Data Science Innovation with Z by HP Workstations and Software Stack by Bradley Franko Hunter Kempf

    • Simplifying MLOps by Taking Storage Worries out of the Equation by Miroslav Klivansky

    • AI for Clinical Care Planning and Decision Support by Sadid Hasan, PhD

    • Using AI for Immunogenicity Potential Assessment in Drug Discovery by Jiayi Cox, PhD

    • Natural Language Processing in Accelerating Business Growth by Sameer Maskey, PhD

    • Machine Learning for A/B Testing by Alex Peysakhovich, PhD

    • Kubernetes - Observability Engineering by Ravi Kumar Buragapu

    • Understanding and Optimizing Parallelism in NumPy-based Programs by Ralf Gommers, PhD

    • Gym and the Future of Reinforcement Learning by J K Terry

    • Data Science in the Cloud-Native Era by Yuan Tang

    • What We’ve Learned Pushing Nearly 100M Hours of GPU Compute by James Skelton

    • ImageNet and its Discontents. The Case for Responsible Interpretation in ML by Razvan Amironesei, PhD

    • Evaluating, Interpreting and Monitoring Machine Learning Models by Ankur Taly, PhD

    • Scaling AI Workloads with the Ray Ecosystem by Robert Nishihara

    • A New Indexing Technique for Quickly Fuzzy-Matching Entire Dataset Records by Dan S. Camper

    • Z by HP Panel Discussion on the Diverse Role of Data Science in Education by Max Urbany, Dan Chaney, Kristin Hempstead

  • 3

    Partners Demo Talks

    • Run Azure Machine Learning Anywhere in Multi-cloud or on Premises by Doris Zhong

    • Supercharging Geospatial Analysis In Your Data Science Workflow by Shan He

    • Building Provenance and Reproducibility into ML Systems by Adam Pocock, PhD

    • A New Data Format to Deliver Real-Time Data at Massive Scale by Denis Coady

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

    • The Hidden Layers of Tech Behind Successful Data Labeling by Glen Ford

    • Supercharging MLOps with Composability, Automation, and Scalability by Aurick Qiao, PhD, Tong Wen, PhD

    • Introduction to WSL2 for Data Science with Z by HP by Akram Dweikat

    • MLOps: From 0-60 with Pachyderm by Jimmy Whitaker

    • What to Do When Your Data Gets Big by Nathan Ballou

    • InfluxDB: The Database for Your Time Series Data Science Problems by Anais Dotis-Georgiou

    • Data Observability in 10 Minutes by Kevin Hu

    • Reimagine Clinical Research with the Power of Artificial Intelligence by Sanjay Patil

    • Accelerating MLOps with Kubernetes, CI/CD & GitOps by Audrey Reznik

  • 4

    ODSC Workshops & Tutorials

    • Object detection with Red Hat OpenShift Data Science by Audrey Reznik Prasanth Anbalagan

    • Analyzing Sensitive Data Using Differential Privacy by Ashwin Machanavajjhala, PhD and Michael Hay, PhD

    • Vector Database Workshop Using Weaviate by Laura Ham

    • Text Categorization and Topic Modeling by Sanghamitra Deb, PhD

    • Towards Data Scientist - Friendly Natural Language Processing by Sepideh Seifzadeh and Monireh Ebrahimi

    • Prepare Data Science/ML Pipelines with Ease, Speed Following Best Practices by Ido Michael

    • A Tutorial on Contemporary Machine Learning Risk Management by Patrick Hall

    • Building and Deploying the World's Largest Rock/Paper/Scissors Competitive Ladder App in X Minutes with Roboflow and Streamlit by Jay Lowe

    • Tired of Cleaning your Data? Have Confidence in Data with Feature Types by John Peach

    • Streamlit: Next-generation Communication of Data Insights by Adrien Treuille, Phd

    • Full-stack Machine Learning for Data Scientists by Hugo Bowne-Anderson

    • Open-source Tools for Synthetic Data On-Demand by Lipika Ramaswamy

    • Deep Dive Workshop for Apache Superset by Srinivasa Kadamati

    • Quantization in PyTorch by Jerry Zhang

    • Bridging the Gap Between Data Scientists and Business Users by Amir Meimand, PhD

    • Overview of methods to handle missing values by Julie Josse, PhD Gael Varoquaux, PhD

    • Machine Learning for Causal Inference by Stefan Wager, PhD

    • Creating and Operating ML Models from Event-based Data Using Feature Stores and Feature Engines by Dr. Charna Parkey

    • Overview of Geocomputing and GeoAI at Oak Ridge National Laboratory: Exploitation at Scale, Anytime, Anywhere by Dalton Lunga, PhD, Jacob Arndt, Jesse Piburn

    • The Future of Software Development Using Machine Programming by Justin Gottschlich, Ph.D.

    • Hands-on Reinforcement Learning with Ray and RLlib by Richard Liaw, PhD, Christy Bergman, Avnish Narayan

    • Evolution of NLP and its Underpinnings by Chengyin Eng

    • Few-Shot Learning by Isha Chaturvedi

    • Telling stories with data by Gulrez Khan

    • Self-supervised Representation Learning for Speech Processing by Abdel-rahman Mohamed, PhD

    • Self-Supervised and Unsupervised Learning for Conversational AI and NLP by Chandra Khatri

  • 5

    ODSC Trainings

    • NLP Fundamentals by Leonardo de Marchi

    • Transformers & Datasets for Research and Production by Patrick von Platen

    • SQL for Data Science by Mona Khalil

    • Manipulating and Visualizing Data with R by Jared Lander

    • Programming with Data: Python and Pandas by Daniel Gerlanc

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

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

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

    • Transformer Based Approaches to Named Entity Recognition (NER) and Relationship Extraction (RE) by Sujit Pal

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

    • Tutorial: Building and Deploying Machine Learning Models with TensorFlow and Keras by Yong Tang, PhD

    • Introduction to the PyTorch Lightning Ecosystem by Kaushik Bokka Jirka Borovec, PhD

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

  • 6

    Extra Events

    • AI Investors Reverse Pitch by Igor Taber, Sarah Fay, Danel Dayan

    • Women in Data Science Ignite by by Sewalita Duara, Amy E. Holder, Ahn Tran Reshmi Ghosh

Upcoming ODSC Conferences

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