Modern ML: GenAI, Trust, & Path2Prod

Discover the transformative impact of Generative AI and large language models (LLMs) on AI/ML advancements. We will delve into the latest trends and techniques for building modern ML systems and applications. The track will focus on three crucial themes: generative AI, trust, and the path to production.

Focus Areas:

  • Generative AI for LLMs: Explore Retrieval Augmented Generation and its impact on LLMs.
  • Trust and Efficiency: Mitigate risks and enhance the safety and efficiency of LLM-powered applications.
  • Scalability and Optimization: Leverage modern compute stack for scaling AI/ML/LLM workloads.
  • Data-Centric AI Applications: Master the path to production for data-centric AI applications.

Join us to explore these transformative areas that are shaping the future of ML/AI, and gain the knowledge to build more potent and dependable ML systems.


From this track

Session AI/ML

Chronon - Airbnb’s End-to-End Feature Platform

Tuesday Oct 3 / 10:35AM PDT

ML Models typically use upwards of 100 features to generate a single prediction. As a result, there is an explosion in the number of data pipelines and high request fanout during prediction.

Speaker image - Nikhil Simha

Nikhil Simha

Author of "Chronon Feature Platform", Previously Built Stream Processing Infra @Meta and NLP Systems @Amazon & @Walmartlabs

Session AI/ML

Defensible Moats: Unlocking Enterprise Value with Large Language Models

Tuesday Oct 3 / 11:45AM PDT

Building LLM-powered applications using APIs alone poses significant challenges for enterprises. These challenges include data fragmentation, the absence of a shared business vocabulary, privacy concerns regarding data, and diverse objectives among data and ML users.

Speaker image - Nischal HP

Nischal HP

Vice President of Data Science @Scoutbee, Decade of Experience Building Enterprise AI

Session Distributed Computing

Modern Compute Stack for Scaling Large AI/ML/LLM Workloads

Tuesday Oct 3 / 01:35PM PDT

Advanced machine learning (ML)  models, particularly large language models (LLMs), require scaling beyond a single machine.

Speaker image - Jules Damji

Jules Damji

Lead Developer Advocate @Anyscale, MLflow Contributor, and Co-Author of "Learning Spark"

Session AI/ML

Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)

Tuesday Oct 3 / 02:45PM PDT

In this presentation, we will delve into the world of Retrieval Augmented Generation (RAG) and its significance for Large Language Models (LLMs) like OpenAI's GPT4. With the rapid evolution of data, LLMs face the challenge of staying up-to-date and contextually relevant.

Speaker image - Sam Partee

Sam Partee

Principal Engineer @Redis

Session

Unconference: Modern ML

Tuesday Oct 3 / 03:55PM PDT

What is an unconference? An unconference is a participant-driven meeting. Attendees come together, bringing their challenges and relying on the experience and know-how of their peers for solutions.

Session AI/ML

Building Guardrails for Enterprise AI Applications W/ LLMs

Tuesday Oct 3 / 05:05PM PDT

Large Language Models (LLMs) such as ChatGPT have revolutionized AI applications, offering unprecedented potential for complex real-world scenarios. However, fully harnessing this potential comes with unique challenges such as model brittleness and the need for consistent, accurate outputs.

Speaker image - Shreya Rajpal

Shreya Rajpal

Founder @Guardrails AI, Experienced ML Practitioner with a Decade of Experience in ML Research, Applications and Infrastructure

Track Host

Hien Luu

Sr. Engineering Manager @Zoox & Author of MLOps with Ray, Speaker and Conference Committee Chair

Hien Luu is a Sr. Engineering Manager at Zoox, leading the Machine Learning Platform team. He is particularly passionate about building scalable AI/ML infrastructure to power real-world applications. He is the author of MLOps with Ray and the Beginning Apache Spark 3 book. He has given presentations at various conferences such as MLOps World, QCon (SF,NY, London), GHC 2022, Data+AI Summit, XAI 21 Summit, YOW Data!, appy()

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