Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems

Don’t Just Build Models—Build the Platform That Makes Them Scalable

Machine Learning Platform Engineering: Build an Internal Developer Platform for ML and AI Systems | Manning Publications Co., 2026

Most organizations don’t struggle with machine learning ideas.
They struggle with making those ideas repeatable, reliable, and scalable.

Teams build models—but pipelines break.
Experiments succeed—but deployments fail.
Infrastructure exists—but developers can’t use it efficiently.

The missing piece?

A well-designed machine learning platform.

This book shows you how to build it.

Why this book matters

Modern ML and AI systems demand more than isolated workflows. They require:

* Standardized pipelines
* Scalable infrastructure
* Reproducible experimentation
* Seamless deployment and monitoring

Without a platform, every team reinvents the same fragile systems.

Machine Learning Platform Engineering teaches you how to create an internal developer platform (IDP) that transforms ML from experimentation into engineering discipline.

What makes this book different

🧠 Platform Thinking Over Tooling

This isn’t a catalog of tools. It’s a guide to designing systems—where infrastructure, workflows, and developer experience come together.

🛠 End-to-End Architecture

You’ll design platforms that support:

* Data ingestion and feature pipelines
* Model training and experiment tracking
* Continuous integration and delivery (CI/CD)
* Model serving and inference
* Monitoring and lifecycle management

⚙️ Built for Real Organizations

Learn how to create platforms that:

* Enable teams, not block them
* Standardize workflows without limiting flexibility
* Scale across projects and departments

🔍 Developer Experience as a First-Class Concern

A great platform isn’t just powerful—it’s usable. You’ll learn how to design systems developers actually want to use.

What you’ll learn

By the end of this book, you’ll be able to:

* Design and implement ML platforms from the ground up
* Build reusable pipelines for data, features, and models
* Enable self-service infrastructure for ML teams
* Implement CI/CD workflows for ML systems (MLOps)
* Manage model versioning, deployment, and rollback
* Monitor performance, drift, and system health

Inside the book

You’ll explore:

🔹 Platform Foundations

* What defines an ML platform
* Platform vs pipeline thinking
* Key architectural components

🔹 Data and Feature Infrastructure

* Data ingestion and validation
* Feature stores and reuse
* Batch and real-time pipelines

🔹 Training and Experimentation

* Experiment tracking and reproducibility
* Hyperparameter tuning workflows
* Distributed training systems

🔹 CI/CD for Machine Learning

* Automated testing for data and models
* Continuous training and deployment
* Versioning and rollback strategies

🔹 Serving and Inference

* Real-time vs batch inference
* Scalable model serving architectures
* Latency and performance optimization

🔹 Observability and Governance

* Monitoring metrics, logs, and traces
* Detecting drift and anomalies
* Compliance and governance frameworks

🔹 Developer Experience

* Self-service tools and APIs
* Platform abstraction layers
* Documentation and usability design

Who this book is for

* ML engineers and platform engineers
* DevOps engineers working on AI infrastructure
* Data engineers building scalable pipelines
* Technical leaders designing ML systems at scale

If you’re responsible for more than just models—this book is essential.

What you’ll gain

After reading this book, you’ll:

* Think in terms of platforms, not isolated systems
* Build infrastructure that scales across teams and use cases
* Reduce duplication and improve development velocity
* Deliver reliable, production-ready ML systems
* Stand out as an engineer who can operationalize AI at scale

Why it matters

The future of AI isn’t just better models.
It’s better systems that make those models usable.

The organizations that win are those that can build platforms that enable innovation at scale.

Machine Learning Platform Engineering
Design the platform. Empower the teams. Scale AI the right way.

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