Build A Machine Learning Platform (From Scratch)

Machine learning models look great in notebooks, then collapse in production. Ready to build an ML platform that actually delivers? Here's a step-by-step, project-driven guide to building an MLOps-ready platform from scratch.

Inside you'll find: Step-by-step ML pipeline assembly: A true "from-scratch" playbook that assembles an end-to-end MLOps stack. Deploy machine learning models to production: Combine Kubeflow, MLflow, BentoML, Feast, and Evidently without vendor lock-in. Build end-to-end data pipelines: Move seamlessly from raw data to monitored, live predictions. Robust deployment patterns: Serve fast, scalable models that stay responsive under real traffic. Effective monitoring and explainability: Detect drift early and keep stakeholders confident. Three veteran engineers lead you through every layer of modern MLOps. The chapters construct two reference systems, an image classifier and a recommendation engine, while teaching orchestration, training, serving, and monitoring techniques. The actionable items for each concept include sample code, architecture diagrams, and checklists. By the end of this book, you will end up with a reusable blueprint that slashes deployment time, reduces firefighting, and thrives with team growth. You will start shipping platforms that thrive. Ideal for Python-savvy data scientists and software engineers eager to master production-quality machine learning.

ISBN

9781633437333

Format

Hardback

Publisher

Manning Publications, (06 April 2026)

Weight

901 grams

Language

English

Dimensions

235 x 190 x 30 mm

Categories:

General Technology Computing and Information Technology Machine learning Computer programming software engineering