A Coursera specialization from DeepLearning.AI covering the operational side of machine learning: building, deploying, and maintaining ML systems in production, including pipelines and monitoring.

Commenters point to it as the go-to MLOps course for the ML-specific parts of becoming an ML engineer, and note it can be audited for free.

On Reddit (3)

1. Recommendspkscffr/MachineLearning2022in “[D] Machine Learning Engineers - What Does Your Day Involve?”▲ 7

   There's a bunch of things that are useful, it mostly comes down to learning how to be a good software developer and being comfortable with the tools you use. - learn how to use git, Linux, Docker - learn how to write a REST API - read Effective Python and learn more about writing better quality…

2. RecommendsDantonio16r/learnmachinelearning2021in “Can anyone suggest steps for MLOps.”▲ 2

   I suggest you the MLOps specialization on Coursera, the Build Machine Learning Pipelines book too.

3. MentionsAltruistic_Building2r/learnmachinelearning2023in “How to transition from Data Scientist to Machine Learning Engineer?”

   Not that I personally tried, besides best selling ones on udemy and the mlops specialization on coursera

Related courses

• Machine Learning (Andrew Ng)Coursera · 240 recommenders
• Practical Deep Learning for Codersfast.ai · 112 recommenders
• CS231n: Convolutional Neural Networks for Visual RecognitionStanford University · 39 recommenders
• Deep Learning Specialization (Andrew Ng)Coursera · 31 recommenders
• Neural Networks for Machine Learning (Geoffrey Hinton)Coursera · 25 recommenders
• CS229 Machine LearningStanford University · 20 recommenders
• Learning From DataCaltech · 20 recommenders
• CS188 Introduction to Artificial IntelligenceUC Berkeley · 18 recommenders