Course curriculum

  • 1

    ODSC East Keynotes

    • Setting Up Text Processing Models for Success: Formal Representations versus Large Language Models by Carolyn Rosé, PhD

    • Deep Reinforcement Learning in the Real World: From Chip Design to LLMs by Anna Goldie

    • Social and Ethical Implications of Generative AI by Abeba Birhane
  • 2

    ODSC Talks

    • Trial, Error, Triumph: Lessons Learned using LLMs for Creating Machine Learning Training Data by Matt Dzugan

    • What it Takes to Stabilize a GenAI-first, Modern Data Lake in a Big Company: Provision 20,000 Ephemeral Data Lakes Annually by Moses Lee

    • End-to-End Speech Recognition: The Journey from Research to Production by Tara Sainath, PhD

    • Data Automation with LLM by Rami Krispin

    • Applying Responsible Generative AI in Healthcare by David Talby, PhD

    • Shifting Gears to LLMOps_ Understanding the Challenges in MLOps for LLMs by Noel Konagai

    • Navigating the Landscape of Responsible AI_ Principles, Practices, and Real-World Applications by Rajiv Avacharmal

    • Cost Containment A Critical Piece of your Data Team's ROI by Lindsay Murphy

    • Leveraging Predictive Models and Data Science to Optimize Information Retrieval Systems by Hareen Venigalla and Vidhya Suresh

    • From Research to the Enterprise: Leveraging Large Language Models for Enhanced ETL, Analytics, and Deployment by Ines Chami

    • Machine Learning Across Multiple Imaging and Biomarker Modalities in the UK Biobank Improves Genetic Discovery for Liver Fat Accumulation by Sumit Mukherjee

    • How to Become a True Dataviz Pro by Nick Desbarats

    • Optimizing Workplace with AI and Generative Bots by Aleksandra Przegalinska and Tamilla Triantoro

    • Abstracting ARM/x86 CPUs and NVIDIA/Neuron Hardware Accelerator Allocation for Containerized ML App by Yahav Biran

    • The Promise of Edge ML_ Bringing Your Model to Your Data by David Aronchick

    • Resisting AI by Dr. Dan McQuillan

    • Build GenAI Systems, Not Models by Hugo Bowne-Anderson

    • Moving Beyond Statistical Parrots - Large Language Models and their Tooling by Ben Auffarth, PhD

    • AI Resilience Upskilling in an AI Dominant Environment by Leondra Gonzalez

    • Unlocking the Unstructured with Generative AI_ Trends, Models, and Future Directions by Jay Mishra

    • Deciphering Data Architectures (choosing between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh) by James Serra

    • Beyond Theory: Effective Strategies for Bringing Generative AI into Production by Heiko Hotz

    • The 12 Factor App for Data by James Bowkett

    • Generative AI for Social Good by Colleen Molloy Farrelly

    • CodeLlama Open Foundation Models for Code by Baptiste Roziere

    • Build-a-Byte: Constructing Your Data Science Toolkit by Jarai Carter, PhD

    • Programming LLMs for Business Application is Way Better Than 'Tuning' Them by Tsvi Lev

    • Mastering Real-time Processing While Effectively Scaling Data Quality with Lambda or Kappa Architecture by Vipul Bharat Marlecha and Sreyashi Das

    • Clean as You Go Basic Hygiene in the Modern Data Stack by Eric Callahan

    • Data Engineering in the Age of Data Regulations by Alex Gorelik
  • 3

    ODSC Workshops & Tutorials

    • Stable Diffusion - Advancing the Text-to-Image Paradigm by Sandeep Singh

    • Build AI Assistants with Large Language Models by James Busche and Rafael Vasquez

    • LLM Best Practises Training, Fine-Tuning and Cutting Edge Tricks from Research by Sanyam Bhutani

    • Feature Stores in Practice Build and Deploy a Model with Featureform, Redis, Databricks, and Sagemaker by Simba Khadder

    • Topological Deep Learning Going Beyond Graph Data by Dr. Mustafa Hajij

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

    • Prompt Engineering with Llama 3 by Amit Sangani

    • Everything About Large Language Models Pre-training, Fine-tuning, RLHF State of the Art by Chandra Khatri

    • No-Code and Low-Code AI_ A Practical Project Driven Approach to ML by Gwendolyn D. Stripling, PhD

    • How to Practice Data-Centric AI and Have AI Improve its Own Dataset by Jonas Mueller

    • Deploying Trustworthy Generative AI by Krishnaram Kenthapadi

    • Graphs: The Next Frontier of GenAI Explainability by Amy Hodler and Michelle Yi

    • Operationalizing Local LLMs Responsibly for MLOps by Noah Gift

    • Introduction to Linear Regression using Spreadsheets with Real Estate Data by Roberto Reif

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

    • Idiomatic Pandas by Matt Harrison

    • Enabling Complex Reasoning and Action with ReAct, LLMs, and LangChain by Giuseppe Zappia and Shelbee Eigenbrode

    • Lights, Camera, AI Action: Building a Movie Pitch by Combining Generative and Predictive AI with DataRobot by Luke Shulman

    • Introduction to Containers for Data Science - Data Engineering by Michael A Fudge

    • Mastering PrivateGPT Tailoring GenAI for your unique applications by Iván Martínez Toro and Dr. Daniel Gallego Vico