Looking for MLOps courses to attend? The choice can be overwhelming as there are all kinds of great educational content on production machine learning and MLOps. 

We put together five great online MLOps courses for data scientists and ML engineers to join in 2023. We looked to include courses that provide the most value to the community: the ones that are practice-oriented and those that are free or where the content can be accessed without a fee. We hope the list will help you choose your next learning challenge!

If we’re missing some great MLOps courses, please let us know (our Discord is the best place to do it). 

Disclaimer: all information about the listed courses comes from their websites. We simply put it together.

ML Engineering for Production by Andrew Ng

The next cohort starts on October 3, 2023.  
Author: Andrew Ng
Cohort duration: 4 months (5 hours a week)
Course certificate: yes (requires a Coursera subscription).
Price: $49/month, but content can be accessed for free.
The course syllabus is available here.

ML Engineering for Production (MLOps) specialization from Andrew Ng is a conceptual MLOps course that serves as a great introduction to production machine learning. The specialization is for early-career ML practitioners and is made up of 4 courses:

• Introduction to ML in production,
• ML data lifecycle in production,
• ML modeling pipelines in production,
• Deploying ML Models in production.

This MLOps course teaches how to design an ML production system end-to-end: build data pipelines, establish a model baseline, develop and deploy ML models, and continuously monitor a production system.

The specialization is available on Coursera and requires a subscription if you want to earn a certificate of completion. A Coursera subscription currently costs $49 per month. However, If you only want to read and view the course content, you can audit the course for free.

ML Engineering for Production specialization videos are available on Coursera. 

Open-source ML observability course 

The 2023 cohort starts on October 16, 2023. You can also go through all materials at your own pace. 
Author: Evidently AI
Cohort duration: 7 weeks
Price: free.
Course certificate: yes (for cohort participants).
The course syllabus is available here.

Open-source ML observability course is a new hands-on MLOps course created by the team that develops Evidently, an open-source Python library to evaluate, test, and monitor ML models in production. 

The course focuses on a particular aspect of MLOps: production ML model monitoring. It covers the key concepts of ML monitoring and observability, different types of evaluations, and how to integrate them into ML pipelines. Throughout the MLOps course, students follow end-to-end code examples and work with open-source tools like Evidently, MLflow, Airflow, and Grafana. 

The course is free and takes seven weeks to complete. 

Students can enroll in the 2023 cohort to earn a certificate or join at any point and study at their own pace. 

The course materials will be available on the Evidently YouTube channel. You can register here to get notified.

MLOps Zoomcamp

The 2023 cohort is completed, but you can freely access all course materials.  
Author: DataTalks.Club 
Cohort duration: 10 weeks
Price: free.
Course certificate: yes (for cohort participants).
The 2023 course syllabus is available here.

‍MLOps Zoomcamp is a free MLOps course from DataTalks.Club. It teaches MLOps best practices and covers experiment tracking and model management, orchestration and ML pipelines, ML model deployment, and ML model monitoring. Throughout the course, students master open-source MLOps tools like MLflow, Prefect, Grafana, and Evidently. 

This MLOps course will be useful for data scientists, ML engineers, and software and data engineers interested in production ML.

The course includes ten weeks of video lessons, code practice, and a final project with peer review. 

MLOps Zoomcamp materials are available in the course’s GitHub repo and YouTube playlist. 

Made with ML

The dates of the 2023 live cohort are not yet known. However, you can freely access course materials.  
Author: Goku Mohandas
Cohort duration: 11 weeks
Price:  $150/person for the live cohort, but content is available for free.
Course certificate: no
The course curriculum is available here.

Made with ML is an MLOps course developed and taught by Goku Mohandas. The course teaches how to build an end-to-end machine learning system connecting all the MLOps components: tracking, testing, serving, orchestration, monitoring, etc. The learning materials include course notes together with code examples. 

Two options are available for attending this MLOps course:

• Joining a live cohort with in-person lessons, Q&A, and computing (GPUs). This option takes one day to complete the course and costs $150 per person. 
• Taking the course in a self-paced mode. This option has no time commitment and is free. All the computing can be done locally on a laptop. 

Made with ML course materials are freely available on the course’s website. 

Full Stack Deep Learning course

The 2023 dates are not known. However, you can access videos and course notes from 2022.  
Author: The Full Stack 
Cohort duration: 11 weeks
Course certificate: no
Price: free.
The 2022 course curriculum is available here.

The Full Stack Deep Learning course started in 2018 as a three-day MLOps boot camp on the Berkeley University campus. After five years of updating and improving course materials, it is freely available as a full-scale online MLOps course.

The course covers the fundamentals of MLOps, such as planning and managing ML projects, choosing the right infrastructure and tooling, data management, deploying ML models at scale, and continuous ML monitoring. It includes 9 lectures, 8 code labs, and a final portfolio project. 

This MLOps course is built for ML researchers and engineers, data scientists, and project managers on ML teams.

Full Stack Deep Learning course materials are freely available on their website. 

[fs-toc-omit]Want to learn more about ML monitoring?

Sign up for our Open-source ML observability course. Designed for data scientists and ML engineers. Yes, it's free!

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