If you enroll in an average machine learning or data science course, chances are, you are only going to hear about algorithms. Some are more practical and teach you how to use certain frameworks and train models, but the majority don’t go beyond that.
However, this is only a small part of the entire machine learning pipeline. As an engineer or data scientist, your task rarely begins and ends with method development. Rather, most time is spent with data engineering and model serving infrastructure management.
As the community of professionals soon realized this, an increasingly large effort was placed to manage machine learning operations throughout the entire life cycle. Thus, to the analogy of DevOps, the field of MLOps has slowly emerged.
During the evolution of a technical field, its accessibility goes through three major phases. First, upon its inception, knowledge is not readily available if you are not at the forefront of the efforts. Second, where the first textbooks are written and courses are created, but best practices are still not clear and the information is scattered in several places. Finally, a field reaches a certain level of maturity when it becomes part of a standard curriculum. Deep learning and machine learning are already there.
However, MLOps is still in the second phase. There are several great learning resources out there, but it can take quite a while to find and filter them. This post aims to do this work for you: we are going to take a look into three of the best places to learn the fundamentals of MLOps.
Let’s get started!
1. The Full Stack Deep Learning course
Originally taught at as a boot camp in Berkeley, the Full Stack Deep Learning course has become one of the most comprehensive introductions to the more practical side of machine learning.
Recently, they have made the entire lecture series available online, along with the projects
Instead of the theory and model training, their curriculum contains the following lectures:
- Setting up machine learning projects
- Infrastructure and tooling
- Data management
- Machine learning teams
- Training and debugging
- Testing and deploying
Overall, this is the best introduction to the field in my opinion. The material taught runs wide rather than deep, but in the end, you’ll realize how vast MLOps is and how much you don’t know.
2. Machine Learning Engineering by Andriy Burkov
The Machine Learning Engineering book is written by Andriy Burkov, which perfectly complements the Full Stack Deep Learning course. The book itself is distributed according to the “read first, buy later” principle, which means that if it provided you value, you can support the author by purchasing.
Instead of going into the toolkit of MLOps, the book offers more of a “theory of the practice” approach, providing you an overview of the problems, questions, and best practices of machine learning problems.
If you are interested, you should check out The Hundred-Page Machine Learning Book, which is a more theory-focused reading from the same author.
3. Awesome MLOps and production machine learning GitHub lists
Probably you have encountered the concept before, but if this is the first, an Awesome list is a thematic curated catalog of resources, hosted in the form of a GitHub repository containing only a README file.
In our case, two very useful lists are the Awesome MLOps and the Awesome Production Machine Learning. While the former focuses on learning resources, the latter complements it with an emphasis on tooling.
These lists are useful when you already have a comprehensive view of the MLOps field and you would like to specialize in a given subdomain, such as model serving and monitoring.
As you can already see, managing machine learning projects throughout their entire life cycle is astoundingly complex. However, with this knowledge below your belt, you’ll be ready to tackle many of its challenges. So, go and learn awesome things 🙂
If you are interested in MLOps, check out our other articles about the topic!