Machine Learning and Go (Golang): A Practical Guide

The book "Machine Learning and Go (Golang)" is the first practical guide in Russian that combines the power of the Go language with modern machine learning methods. If you thought ML was only Python, this book will break those stereotypes. Go offers high performance, simple parallelization, and easy deployment, making it an ideal choice for production machine learning systems.

What is this book about?

The book covers the full ML workflow in Go:

• Data collection and preparation — working with CSV, JSON, databases, data cleaning and normalization in Go.
• Implementing algorithms from scratch — linear regression, logistic regression, decision trees, k-nearest neighbors. You don't just use ready-made libraries; you understand how algorithms work.
• Using popular Go libraries — golearn, gonum, gorgonia for neural networks, smile for statistics.
• Model evaluation and validation — cross-validation, metrics (precision, recall, F1), ROC curves.
• Integration with Python — how to call Python models from Go and vice versa, using ONNX for cross-platform model exchange.
• Production — deploying ML services via REST API, gRPC, working with Kafka for streaming data, monitoring models in production.

Who is this book for?

• Go developers who want to add ML to their stack without switching to Python.
• ML engineers looking for alternatives to Python for high-load systems.
• Students and teachers studying machine learning with modern languages.
• Anyone who wants to understand how to build production-ready ML systems without heavy frameworks.

What will you learn?

• How to implement basic ML algorithms in Go from scratch.
• How to use ready-made libraries to speed up development.
• How to process large volumes of data using goroutines and channels.
• How to package a model in Docker and deploy it in Kubernetes.
• How to test and debug ML code in Go.

Why Go for ML?

Go compiles to a binary, requires no virtual machine, has built-in concurrency support, and an excellent standard library. The book shows how to turn these advantages into real ML solutions: from simple predictions to complex recommendation systems.

Each chapter contains practical code examples that you can run immediately. By the end of the book, you will create a full-fledged prediction service ready for industrial use.

The book is suitable for levels from Junior to Senior — the material is structured so that beginners will grasp the basics, while experienced developers will find advanced optimization and deployment techniques.

Dive into the world of machine learning with Go and discover a new way to build smart and fast applications!