Course curriculum

  • 1

    ODSC West Keynotes

    • Provably Beneficial Artificial Intelligence by Stuart Russell, PhD

    • How Companies Have Achieved Business Benefits with Kubernetes Powered MLOps by Abhinav Joshi and Will McGrath

    • The Future of Data of Science with Z by HP by Bruce Blaho and Andrew Kemp

    • Making AI work for business by JR Gauthier, PhD

    • Instilling Interpretability and Explainability into AI Projects by Scott Reed

    • Increasing Accuracy with Human Labeling and Weak Learning by Elliot Branson

    • Why Data Lakes are critical for AI, ML and IoT by Brian Flūg

    • Unlocking Climate-Related Data Through Open Source and Data Mesh Architecture by Vincent Caldeira and Erik Erlandson

    • Changing the Narrative: The Importance of Responsible AI and Human-AI Collaboration by Lama Nachman

    • MLOps Spotlight_ Scaling NLP Pipelines at IHS Markit by Yaron Haviv and Nick Brown

    • How Can You Trust Machine Learning by Carlos Guestrin, PhD

    • Break Out of your Data Bubble by Victor Ghadban

    • What is MLOps, DataOps, and DevOps by Manasi Vartak, PhD

    • 100x More Features at Scale with Feature Engineering Automation by Sharada Narayanan
  • 2

    ODSC Demo Talks

    • How to improve data workload flexibility while lowering cloud data lake costs by over 50% by Arpan Roy

    • Trustworthy Decision Management: How Explainable, Predictive Decision Making Can Help Us Trust Our Decision Models by Jacopo Rota

    • Architecting for Modern Analytics Applications by Zeke Dean

    • Z by HP’s Workstation Data Science Solutions by Lenny Isler

    • Profiling and Optimizing PyTorch Applications with the PyTorch Profiler by Sabrina Smai

    • Vertica Accelerator – The Fastest SaaS Analytics and Machine Learning – from Start to Finish by Michael Bowen

    • Automate machine learning tasks with OCI Data Science Jobs by Lyudmil Pelov

    • Deliver AI & ML Models Faster, with Verta by Anthony Lee

    • Proactive Data Quality_ Why Culture Comes Before Tools by Tim Woods

    • Building ML and AI Applications with a Purpose-Built Time Series Database by Sam Dillard

    • Best Practices of Effective ML Teams by Carey Phelps

    • A Graph Data Science Framework for Enterprise by Stuart Laurie

    • DataRobot AI Cloud Demo_ Massive Business Impact from Extreme Automation by Andrea Kropp

    • Getting Started with Dask Using Saturn Cloud by Mitali Sanwal

    • Federated SQL with LiveRamp Safe Haven by Grzegorz Gawron

    • An Overview of Arize AI’s ML Observability Platform by Gabriel Barcelos

    • Streamlining Analytics with the S&P Global Marketplace Workbench by James Olejniczak

    • Teaching Data Science Effectively by Robert Schroll, PhD

    • Analyzing NVMO Mobile Signal Data with Accelerated Analytics by Joe Gifford

    • Weak Supervision in Practice by Patrick Kolencherry

    • Portable, light-weight, end-to-end autoML_ All the power, none of the pain by Alex Robson, PhD

    • Real-Time Feature Engineering with a Feature Store by Adi Hirschtein

    • Metrics Store as an Interface to Data by Allegra Holland

    • The Role of External Data in ML and BI Success by Victor Ghadban

    • See How AtScale's Semantic Layer Impacts BI & AI Performance on Popular Cloud Data Platforms by Daniel Gray
  • 3

    Women in Data Science Ignite

    • Women in Data Science Ignite