With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.

Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.

You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:

Learn the MLOps process, including its technological and business value

Build and structure effective MLOps pipelines

Efficiently scale MLOps across your organization

Explore common MLOps use cases

Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI

Build production applications with LLMs and Generative AI, while reducing risks, increasing the efficiency, and fine tuning models

Learn how to prepare for and adapt to the future of MLOps

Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy

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