Implementing MLOps in the Enterprise: A Production-First Approach

Building operational machine learning pipelines is essential for businesses to meet demand for scaling, real-time access, and other capabilities. This practical guide helps companies bring data science to life for different real-world MLOps scenarios, taking a production-first approach to design a continuous operational pipeline. By automating as many components as possible and making the process fast and repeatable, pipelines can scale to match organizational needs.

Format: Paperback / softback
Length: 350 pages
Publication date: 19 December 2023
Publisher: O'Reilly Media

As businesses seek to scale their operations, real-time access, and other advanced capabilities, the need for operational machine learning pipelines becomes increasingly crucial. This practical guide aims to empower companies to bring data science to life in various real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will gain valuable insights on how to overcome challenges that hinder the deployment of ML models in production.

Authors Yaron Haviv and Noah Gift adopt a production-first approach, emphasizing the design of a continuous operational pipeline that seamlessly integrates various components and practices. By automating as many processes as possible and ensuring a fast and repeatable pipeline, companies can scale their MLOps capabilities to match their organizational needs. This book will guide readers through the MLOps process, highlighting its technological and business value. It will provide practical guidance on building and structuring effective MLOps pipelines, efficiently scaling MLOps across organizations, exploring common MLOps use cases, and developing MLOps pipelines for hybrid deployments, real-time predictions, and composite AI.

Furthermore, readers will learn how to prepare for and adapt to the future of MLOps, effectively utilizing pre-trained models like HuggingFace and OpenAI to complement their MLOps strategy. By leveraging the power of operational machine learning pipelines, businesses can unlock new opportunities for growth and innovation in the ever-evolving landscape of data science and machine learning.

Weight: 654g
Dimension: 177 x 233 x 23 (mm)
ISBN-13: 9781098136581