Below there are several resources which will help you get started (from beginner to expert). Depending on your level, you can choose where to start solving complex, but interesting tasks for you:

Intermediate

Books

Machine Learning System Design by Valerii Babushkin and Arseny Kravchenko ⭐

Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.

Designing Machine Learning Systems by Chip Huyen ⭐

This book will help you tackle scenarios such as:

• Engineering data and choosing the right metrics to solve a business problem
• Automating the process for continually developing, evaluating, deploying, and updating models
• Developing a monitoring system to quickly detect and address issues your models might encounter in production
• Architecting an ML platform that serves across use cases
• Developing responsible ML systems

Courses

CS 329S: Machine Learning Systems Design ⭐

This course aims to provide an iterative framework for developing real-world machine learning systems that are deployable, reliable, and scalable. You will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects such as team structure and business metrics.

Tutorials

MLOps Guide by Arthur Olga, Gabriel Monteiro, Guilherme Leite and Vinicius Lima

This site is intended to be a MLOps Guide to help projects and companies to build more reliable MLOps environment. This guide should contemplate the theory behind MLOps and an implementation that should fit for most use cases.

Advanced