MLOps is one of the hottest topics today, and for a good reason.
Bringing AI models to production, through research to deployment to monitoring there is a long and complex way.
Engineering-wise you’d like to provide the data scientists with the best tools and ease the path to becoming highly productive.
There are courses, videos, and tutorials scattered around the net.
In this post, you’ll get to know many of them – and choose your own path.
10 lectures from the IDC university. The first three describe the challenges in the various stages of the ML Lifecycle, and the rest of the lessons focus on aspects of the MLOps world that help solve some of these problems, such as Fairness, XAI, and more
The course offers an iterative approach to building deployable, reliable, and scalable machine-learning systems.
You will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, and 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. In the process, students will learn about important issues including privacy, fairness, and security.
In this course, you’ll learn how to start from a concept, build, and maintain integrated systems that continuously operate in production.
In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. In the end, you’ll be involved in the development of cutting-edge AI technology and solving real-world problems using your new production-ready skills
A project-based course on the foundations of MLOps to responsibly develop, deploy and maintain ML.
Textual git-based course in bite-size pieces.
In this course, you’ll learn how to design and implement an end-to-end ML and data observability pipeline.
instructor: Alexey Grigorev
FYI – you have to apply to join the classes