Dataframes Are All You Need: MLOps on Easy Mode

Explore the power of dataframes in MLOps through this comprehensive 57-minute talk by Jay Chia, Co-Founder of Eventual, at MLOps Community Meetup #124. Dive into the critical role of dataframes in building and maintaining datasets for machine learning workflows. Learn how dataframes serve as flexible interfaces for complex data processing, analytics, I/O, and feeding ML training pipelines, using Daft (www.getdaft.io) as a practical example. Gain insights into handling unstructured data, modern ML requirements, and the bare-bones ML stack. Watch a live Daft demo and discover its roadmap, including features like streaming and syntax improvements. Understand how dataframes can simplify MLOps processes and enhance your machine learning projects.

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[] Introduction to Jay Chia
[] Join the virtual LLMs in Production Conference Part 2 and In Person San Francisco Workshop - Deploy and Scale LLM-based applications today!
[] Dataframes Are All You Need: MLOps on Easy Mode
[] Jay's background
[] Dataframes
[] What is a Dataframe?
[] Different Dataframes
[] Table Stakes
[] Let's talk MLOps
[] "Unstructured" data
[] The bare-bones ML Stack
[] Daft Demo
[] Modern ML requirements
[] The bare-bones ML Stack
[] Daft Roadmap
[] Giving access to the repo notebook
[] Streaming
[] Syntax and library expression
[] S3 bucket and the data frames
[] Trading off latency and throughput in the library
[] Build another data frame Library
[] Daft for Eventual
[] System streaming mode
[] Wrap up