My go-to MLOps Resources!

My go-to MLOps Resources!
Photo by CDC / Unsplash

Here is my go-to resources for MLOps.

Designing ML Systems book by Chip Huyen:

In this book, you’ll learn how to design a machine learning (ML) system that is reliable, scalable, maintainable, and adaptive to changing environments and business requirements. The author takes a holistic approach to designing ML systems that are both complex and unique.

Book link

MLOps Zoomcamp by

They call it Zoomcamp because all of the content they’re using Zoom and it’s Free. (also it has recordings) 📽️

You can learn MLOps topics;

  • Processes
  • Training models (experiment tracking and building ML pipelines)
  • Serving models (online, batch, streaming)
  • Monitoring
  • Best practices

Course link

Full Stack Deep Learning course:

Join thousands of learners from  UC Berkeley,  University of Washington, and  all over the world to learn how best practices for building ML-powered products.

Join a synchronous cohort to participate in lectures, code interactive labs, and create your own ML-powered application as a final project, or just  follow along on your own time for free.

Reliable Machine Learning book:

How to run an efficient and reliable machine learning (ML) system. This book shows data scientists, software and site reliability engineers, product managers, and business owners how to run ML effectively and accountably. Read on to learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. Link

Practical MLOps book:

Machine Learning Operations (MLOps) is the process of getting your machine learning models into production. This guide explains what MLOps is and how it differs from DevOps. It also gives you a foundation in data science and Python to build a foundation for ML tools and methods. Link

Made with ML - Goku Mohandas:

Learn how to combine machine learning with software engineering to develop, deploy & maintain production ML applications.

Made with ML

Serverless ML Course

Serverless Machine Learning (ML) makes it easy to build a system that uses ML models to make predictions. You do not need to be an expert in Kubernetes or cloud computing to build an end-to-end service that makes intelligent decisions with the help of a ML model.

Course link