MLOps Engineering: Curriculum for Success

MLOps, or Machine Learning Operations, is a crucial field that focuses on streamlining the process of building, deploying, and maintaining machine learning models in production environments. As an MLOps engineer, you’ll need to have a strong understanding of various technologies, tools, and methodologies to be successful in your role. Here’s a comprehensive curriculum that covers all the essential topics for becoming an effective MLOps engineer:

Programming

1. Python: Since MLOps engineers collaborate with machine learning engineers and data scientists, proficiency in Python is vital. Start by reading “Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming 3rd Edition” by Eric Matthes. Practice your coding skills on LeetCode and enroll in Learn Python 3 courses or follow Python fundamentals and Python programming tracks.
2. Bash basics & command line editors: Understanding bash basics is essential for creating CI/CD pipelines, working with Dockerfiles, and more. Read “The Linux Command Line, 2nd Edition” by William E. Shotts and take the Bash mastery course. Familiarize yourself with VIM, a popular command-line editor, by following the VIM beginners guide.

Containerization and Kubernetes

1. Docker: Docker is a widely used containerization platform for code development, model training, and endpoint deployment. Follow the Docker roadmap and take the Full Docker tutorial by Techworld by Nana.
2. Kubernetes: Kubernetes is crucial for machine learning model training, model endpoint deployment, and serving dashboards. Follow the Kubernetes roadmap and take the Kubernetes course by freeCodeCamp.org. Consider taking the Kubernetes mastery course as well.

Machine Learning Fundamentals

1. Course: https://mlcourse.ai/
2. Book: “Applied Machine Learning and AI for Engineers” by Jeff Prosise

MLOps Principles

1. Books:
   • “Designing Machine Learning Systems” by Chip Huyen
   • “Introducing MLOps” by Mark Treveil 𝖺𝗇𝖽 Dataiku
2. Check out the MLOps maturity assessment.

MLOps Components

1. Book: “ML Engineering with Python” by Andy McMahon
2. Suggested courses:
   • Made with ML MLOps course
   • The full stack 7-steps MLOps framework
   • End-to-end machine learning
3. Version control & CI/CD pipelines: Learn Git and GitHub by reading “Learning Git” by Anna Skoulikari and “Learning GitHub Actions” by Brent Laster. Take Git & GitHub for beginners and Taking Python to Production: A Professional Onboarding Guide tutorials.
4. Orchestration: Learn about Mage, Airflow, Kubeflow, and Metaflow. Take the Introduction to Airflow in Python course.
5. Experiment tracking and model registries: Learn about MLflow. Take the MLflow Udemy course and the End-to-end machine learning (MLflow piece) course.
6. Data lineage and feature stores: Explore Feast and DVC. Take the Creating a feature store with Feast part 1, part 2, part 3 tutorials.
7. Model training & serving: Consider using AWS Sagemaker, Azure ML, Vertex AI, FastAPI, Kubeflow pipelines, and KServe. Follow repository suggestions and tutorial suggestions for more information.
8. Monitoring & observability: Learn about Prometheus, Grafana, Evidently.ai, and NannyML. Take the Mastering Prometheus and Grafana course and the Machine learning monitoring concepts, Monitoring machine learning in Python courses.
9. Infrastructure as code: Terraform is the most popular IaC tool. Learn more about it to make your MLOps framework reproducible.

By following this curriculum, you’ll gain a strong foundation in the skills and technologies needed to excel as an MLOps engineer.