Course curriculum

  • 1

    ODSC West Keynotes

    • Welcome message from Sheamus McGovern

    • Building and Using Generative AI Responsibly: Microsoft’s Journey by Sarah Bird, PhD

    • Large Language Models: Past, Present and Future by Thomas Scialom, PhD

    • From AI to GX: The Quantum Leap in Algorithmic Evolution by Jepson Taylor

    • The Ethics of Digital Minds: A baffling new frontier by Nick Bostrom, PhD

  • 2

    ODSC Talks

    • A Semi-Supervised Anomaly Detection System Through Ensemble Stacking Algorithm by Chuying Ma

    • The Crucial Role of Digital Experimentation and A/B Testing in the AI Landscape by Alessandro Romano

    • Causality and LLMs by Robert Osazuwa Ness, PhD

    • Connecting Large Language Models - Common Pitfalls & Challenges by Nils Reimers

    • Evaluating Synthetic Data with Post-Processing Techniques by Samruddhi (Sam) Kulkarni

    • Orchestrating Generative AI Workflows to Deliver Business Value by Hugo Bowne-Anderson, PhD

    • Finetuning, Serving, and Evaluating LLMs in the Wild by Hao Zhang, PhD

    • How to Systematically Evaluate and Improve your Generative AI Application by Beatriz Strollnitz and Daniel Schneider

    • Adversarial Validation and Training in Stock Market Price Prediction by Giacomo Maccagni and Federico Minutoli

    • Evaluating Recommendation Algorithms at Delivery Hero by Manchit Madan

    • Integrating Language Models for Automating Feature Engineering Ideation by Sergey Yurgenson

    • Towards Explainable and Language-Agnostic LLMs by Walid S. Saba

    • Adopting Language Models Requires Risk Management — This is How by Patrick Hall

    • Security First, Create a Robust Machine Learning Model by Teodora Sechkova

    • Representation Learning on Graphs and Networks by Dr. Petar Veličković

    • Building Robust and Scalable Recommendation Engines for Online Food Delivery by Raghav Bali and Vishal Natani

    • PyTorch 2.1 - New Developments by Supriya Rao

    • Generative Ai vs. AGI: Strengths and Weaknesses of Large Language Models by Dr. Ben Goertzel

  • 3

    Partner Solution showcase

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

    • LLMs in Data Analytics: Can They Match Human Precision? by Gerard Kostin

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

  • 4

    ODSC Trainings

    • Generative AI, Autonomous AI Agents, and AGI - How new Advancements in AI will Improve the Products we Build by Martin Musiol

    • Retrieval Augmented Generation (RAG) 101: Building an Open-Source “ChatGPT for Your Data” with Llama 2, LangChain, and Pinecone by Greg Loughnane and Chris Alexiuk

    • Deep Dive into End-to-end MLOps using Weights and Biases by Ayush Thakur and Soumik Rakshit

    • Simplifying Graph Superpowers (with the help of LLMs) by Anthony Mahanna and Jörg Schad, PhD

    • Generative AI by Leonardo De Marchi

    • Statistics for Data Science and Measurement by Brian Caffo, PhD and Babak Moghadas

    • Data Wrangling with Python by Sheamus McGovern

    • Introduction to Math for Data Science by Thomas Nield

  • 5

    ODSC Workshops & Tutorials

    • Missing Data: A Synthetic Data Approach for Missing Data Imputation by Fabiana Clemente

    • Causal AI: from Data to Action by Dr. Andre Franca

    • The AI Paradigm Shift: Under the Hood of a Large Language Models by Valentina Alto

    • Machine Learning using PySpark for Text Data Analysis by Bharti Motwani

    • Deploying Trustworthy Generative AI by Krishnaram Kenthapadi

    • Building Contextual Information Retrieval Systems with Large Language Models (LLMs) by Chaine San Buenaventura

    • GenAI Breakthrough: Fast, High Quality Tabular Data Synthetization by Vincent Granville and Rajiv Iyer

    • Machine Learning for High-Risk Applications - Techniques for Responsible AI by Parul Pandey

    • Automating Business Processes Using LangChain by James Phoenix

    • Recommender Systems Methods and Usage of Graphs for Recommendations by Harshita Asnani