Opening book details…
Can I read Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems (Early Release) on EtoBox?
Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems (Early Release) by Jim Dowling is a nonfiction available to read on EtoBox.
What is Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems (Early Release) about?
Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems. Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store.
Who reads Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems (Early Release)?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Jim Dowling
- Publisher
- O'Reilly Media, Incorporated
- Published
- 2024
- Language
- EN
- ISBN
- 9781098165222
- Category
- nonfiction
- Subjects
- Computer Science, Artificial Intelligence (Ai), Stem
More by Jim Dowling
Browse all works by Jim Dowling
Similar books
- Reliable Machine Learning (Seventh Early Release) — Cathy Chen & Niall Richard Murphy & Kranti Parisa & D. Sculley & Todd Underwood (2022)
- Building Machine Learning Systems with Python — Luis Pedro Coelho Willi Richert (2013)
- Scaling Machine Learning with Spark (Third Early Release) — Adi Polak (2022)
- Building Feature Extraction with Machine Learning : Geospatial Applications — Aithal, Bharath H. & Prakash, P.S. (2023)
- Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines — Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu, Catherine Nelson (2024)
- Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale — Jayanth Kumar M. J (2022)