9 Sessions from ODSC West That We Can’t Stop Talking About 9 Sessions from ODSC West That We Can’t Stop Talking About
We wrapped up ODSC West 2023 two weeks ago, and we are still talking about our favorite sessions and the speakers... 9 Sessions from ODSC West That We Can’t Stop Talking About

We wrapped up ODSC West 2023 two weeks ago, and we are still talking about our favorite sessions and the speakers who made us laugh or go “ah-ha!” There were far too many such sessions to include all below. But please enjoy this small taste of ODSC West highlights: 

Human-Centered Artificial Intelligence 

Peter Norvig |  Engineering Director at Google | Education Fellow at Stanford Institute for (HAI)

There has been incredible technical progress in AI applications in recent years. In this Keynote talk from Peter Norig, you’ll explore the human side of this progress. In particular, Peter focuses on how we can gain confidence that our applications will be fair, just, truthful, beneficial, and well-stirred for their users, the other stakeholders, and society at large.

Prompt Optimization with GPT-4 and Langchain

Mike Taylor | Owner | Saxifrage

In this session, Mike Taylor discusses how you can use prompt engineering at scale – as part of a template, workflow, or product. Prompting at scale requires that you run that prompt 20-30 times via the GPT-4 API to see how often it fails, as well as rigorously A/B testing your prompt against alternative approaches to find what really makes a difference to results. You’ll learn how Langchain can help you build a consistent system for running, monitoring, and measuring the performance of your prompts, so you can optimize them against your success metric.

Representation Learning on Graphs and Networks

Dr. Petar Veličković | Staff Research Scientist | DeepMind 

This session attempts to give you several “bird’s eye” views on GNNs. You’ll learn about the utility of graph representation learning and derive GNNs from first principles of permutation invariance and equivariance. We will discuss how we can build GNNs that are not strictly reliant on the input graph structure.

Bridging the Interpretability Gap in Customer Segmentation

Evie Fowler | Senior Data Scientist | Fulcrum Analytics

In this talk, you’ll explore a new, hybrid approach which combines the best aspects of both rules-based and machine learning-driven approaches to customer segmentation. Bridging the gap between accuracy and simplicity, this hybrid method offers the precise identification of customer groups created by machine learning clustering methods and the simple business profiles yielded by rules-based segmentation methods. This allows data scientists to fulfill their role of identifying previously undiscovered relationships between data elements while still catering to the goals of business stakeholders.

Aligning Open-source LLMs Using Reinforcement Learning from Feedback

Sinan Ozdemir | AI & LLM Expert | Author | Founder + CTO at LoopGenius

With LLMs like ChatGPT and Llama-2 revolutionizing the field of AI, mastering the art of fine-tuning these models for optimal human interaction has become crucial.

In this session, you will focus on the core concepts of LLM fine-tuning, with a particular emphasis on reinforcement learning mechanisms. The workshop will provide a comprehensive understanding of the challenges and intricacies involved in aligning LLMs. By the workshop’s conclusion, attendees will be well-equipped to harness the power of open-source LLMs effectively, tailoring their models to meet the specific demands of their industries or domains. 

The AI Paradigm Shift: Under the Hood of a Large Language Models

Valentina Alto | Azure Specialist – Data and Artificial Intelligence | Microsoft

This workshop will help you develop an understanding of Generative AI and Large Language Models, including the architecture behind them, their functioning and how to leverage their unique conversational capabilities. You will also become familiar with the concept of LLM as a reasoning engine that can power your applications, paving the way to a new landscape of software development in the era of Generative AI. Finally, we will cover some examples of LLM-powered applications in Python using popular AI orchestrators, such as LangChain.

Stable Diffusion: A New Frontier for Text-to-Image Paradigm

Sandeep Singh | Head of Applied AI/Computer Vision | Beans.ai

This session will introduce you to Stable Diffusion, which is able to generate high-quality images from text descriptions, and it is well-suited for a variety of applications, such as creative content generation, product design, and marketing.

 By the end of this session, you will be able to:

 – Understand the basics of Stable Diffusion and how it works.

 – Know the whole landscape of tools and libraries for the Stable Diffusion domain.

 – Generate images from text descriptions using Stable Diffusion.

 – Apply Stable Diffusion to their own projects and workflows.

 – Understand the process of fine-tuning open-source models to achieve tasks at hand. 

Building Generative AI Applications: An LLM Case Study

Michelle Yi | Board Member | Women in Data

This talk will dive into the end-to-end process of and framework for building a generative AI application, leveraging a fun and engaging case study with open-source tooling (e.g., HuggingFace models, Python, PyTorch, Jupyter Notebooks). You will be guided through key stages from model selection and training to deployment, while also addressing fine-tuning versus prompt-engineering for specific tasks, ensuring the quality of output, and mitigating risks. You’ll explore the challenges encountered and emerging solutions and architectures developed. 

Causal AI: from Data to Action

Dr. Andre Franca |  CTO | connectedFlow

Explore and demystify the world of Causal AI for data science practitioners in this session, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. You’ll cover topics like

  •  From shapley to DAGs: the dangers of using post-hoc explainability methods as tools for decision making, and how traditional ML isn’t suited in situations where want to perform interventions on the system.
  •  Discovering causality: how do we figure out what is causal and what isn’t, with a brief introduction to methods of structure learning and causal discovery
  • Optimal decision making: by understanding causality, we now can accurately estimate the impact we can make on our system – how to use this knowledge to derive the best possible actions to make?

Weren’t able to join us at ODSC West? No worries! ODSC East 2024 is just around the corner (well 5+ months away, but who’s counting). Get your in-person or virtual pass now and save 75%!



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