Course curriculum

  • 1

    ODSC East Keynotes

    • Secrets of Successful AI Projects by Pedro Domingos, PhD

    • Infuse Generative AI in your apps using Azure OpenAI Service by Eve Psalti

    • Transforming Drug Discovery using Digital Biology by Daphne Koller, PhD
  • 2

    ODSC Talks

    • Interactive Explainable AI by Meg Kurdziolek, PhD

    • Trustworthy Machine Learning: Robustness, Privacy, Generalization, and their Interconnections by Bo Li, PhD

    • Semantic Search by Nils Reimers

    • Leverage Reviews Data for Multi Label Topics Classification in Booking.com by Moran Beladev

    • The Shape of Data_An Overview of Geometry in Data Science by Colleen Molloy Farrelly

    • If We Want AI to be Interpretable, We Need to Measure Interpretability by Jordan Boyd-Graber, PhD

    • Robustness to Adversarial Inputs and Tail Risk via Boosting by Pradeep Ravikumar, PhD

    • Train and Sustain: Why data leaders need to pay attention to HITL by Matt Beale

    • Truth Checker: Generative Large Language Models and Hallucinations by Chandra Khatri

    • The 10 Ways Machine Learning Systems Can Fail and How to Avoid Them by Bhaktipriya Radharapu

    • Applying Responsible AI with Open-Source Tools by David Talby, PhD

    • Containers + GPUs In Depth by Emily Curtin

    • Using AI to detect Anomalies in Robotics at the Edge by Tom Corcoran and Andreas Spanner

    • Reasoning in Natural Language by Dan Roth, PhD

    • Using Data Science to Better Evaluate American Football Players by Eric Eager, PhD

    • Building Robust Graph Embeddings for Massive Real World Graphs by Aishwarya Naresh Reganti

    • Recent Advances in Foundation Models by Irina Rish PhD

    • Responsible AI In Practice by Minsoo Thigpen, Mehrnoosh Sameki, PhD

    • Solving MLOps from First Principles by Dean Pleban

    • Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos by Jeff Clune, PhD

    • Product Classification with Structured Metadata for Online Retail by Kshetrajna Raghavan

    • Text and Code Embeddings by Arvind Neelakantan, PhD

    • Testing Positive Semidefiniteness and Eigenvalue Approximation by David P. Woodruff, PhD

    • Revolutionizing Healthcare with Synthetic Clinical Trial Data by Afrah Shafquat, PhD

    • SQuARE: Towards Multi-Domain and Few-Shot Collaborating Question Answering Agents by Iryna Gurevych, PhD and Haritz Puerto

    • Rein in Your Data with GX OSS by Alex Sherstinsky

    • Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI by Jonas Mueller

    • The Future Is Notebooks by Elijah Meeks and Carol Willing
  • 3

    Partner Demo Talks

    • Ask the Experts! ML Pros Deep-Dive into Machine Learning Techniques and MLOps by Seth Juarez

    • Driving AI Forward: Continental Tire’s Journey to MLOps Excellence by Drazen Dodik

    • The Tangent Information Modeler, time series modeling reinvented by Philip Wauters

    • Introducing Elemeta_OSS meta-feature extractor for NLP and vision by Lior Durahly

    • Accelerating AI ML Initiatives with Knowledge Graph by Greg West

    • On the Scent: Detecting Dogs on Edge Devices With YOLOv8 and Comet by Kristen Kehrer

    • Data-Centric AI: Moving Beyond Model-Centric Approaches with Pachyderm by Jimmy Whitaker
  • 4

    ODSC Career Talks

    • Data-curiosity: How to Create and Nurture a Data-curious Culture in your Organization by Vatsala Sarathy

    • Do You Know About The People Behind The Tools? by Anna Jung
  • 5

    ODSC Business Talks

    • Winning The Room: Creating And Delivering An Effective Data-Driven Presentation by Bill Franks

    • Why do AI Models go Rogue? A Guide to Detect and Fix Silent Model Failures by Ayush Patel
  • 6

    ODSC Workshops & Tutorials

    • Annexing MATLAB Map-Reduce Capability for Big Data Analytic by Oluleye H Babatunde, Ph.D

    • Next-Level Data Visualization in Python by Melanie Veale, PhD

    • Unifying ML With One Line of Code by Daniel Lenton, PhD

    • A Natural Language Processing (NLP) Approach by Melissa Rollot

    • Being well informed: Building a ML Model Observability Pipeline by Rajeev Prabhakar and Anindya Saha

    • The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation by Andrew Zaldivar and Mahima Pushkarna

    • When Privacy Meets AI - Your Kick-Start Guide to Machine Learning with Synthetic Data by Alexandra Ebert

    • Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training by James Demmel, PhD

    • Creating a Custom Vocabulary for NLP Tasks Using exBERT and spaCY by Swagata Ashwani

    • From Big Data to NLP insights: Getting started with PySpark and Spark NLP by Akash Tandon

    • Automate Machine Learning Workflows with PyCaret 3.0 by Moez Ali

    • Mastering Adversarial Evaluation for NLP: A Practical Workshop by Panos Alexopoulos PhD