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
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
Virtual and In-Person