Begin your MLOps learning challenge with these extensive, free resources on GitHub.
Mastering MLOps (Machine Learning Operations) is becoming more critical for companies looking to properly deploy, monitor, and manage their ML models in production. MLOps is a collection of procedures designed to integrate ML system development (Dev) and operation (Ops). Fortunately, the open-source community has generated countless resources to help newcomers understand these ideas and technologies.
Here are five GitHub projects that are indispensable for anyone trying to grasp MLOps:
1. MLOps-Basics
GitHub Link: https://github.com/visenger/awesome-mlops
With this MLOPS-curated course, you will learn a lot about MLOPS. It includes the core of MLops along with Data Engineering. You will get to know about the ML existing systems.
This course also concludes multiple learning experiences:
- MLOps: Model Deployment and Serving
- MLOps: Infrastructure
- Product Management for ML/AI
- Model Governance, Ethics, Responsible AI
2. MLOps examples by Microsoft
GitHub Link: Microsoft/MLOps
The repository includes MLOps end-to-end examples and solutions. A collection of examples demonstrating various end-to-end scenarios for operationalizing ML processes with Azure Machine Learning, including integration with GitHub and other Azure services like Data Factory and DevOps.
3. mlops-zoomcamp
GitHub Link: DataTalksClub/mlops-zoomcamp
One of my favorite courses for learning new concepts through project-based learning. DataTalks.Club’s MLOps course covers the practical aspects of bringing machine learning services into production, including training, experimentation, model deployment, and monitoring. It is intended for data scientists, ML engineers, software engineers, and data engineers who want to understand how to implement machine learning workflows.
4. MLOps Course by Goku Mohandas
GitHub Link: GokuMohandas/mlops-course
The course teaches students how to design, develop, deploy, and iterate on production-grade machine learning systems using best practices, scale ML workloads, integrate MLOps components, and create CI/CD processes for continuous improvement and easy deployment.
5. Serverless ML Course
GitHub Link: featurestoreorg/serverless-ml-course
This course focuses on developing complete Machine Learning systems with serverless capabilities. It allows developers to create predictive services without requiring expertise in Kubernetes or cloud computing. They can do so by writing Python programs and using serverless features, inference pipelines, feature stores, and model registries.
Machine Learning Engineering for Production (MLOps) Specialization by Coursera
Course Link: https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops
This course is curated to specialize the MLops and it includes :
- Design an ML production system from start to finish, including project scoping, data requirements, modeling methodologies, and deployment requirements.
- Create a model baseline, handle idea drift, and demonstrate how to build, deploy, and constantly improve a production-ready ML application.
- Create data pipelines by collecting, cleaning, and verifying datasets. Create a data lifecycle utilizing data lineage and provenance metadata technologies.
- Maintain and manage a continuous production system by implementing best practices and progressive delivery methodologies.
MLOps for Beginners
Course Link: https://www.udemy.com/course/mlops-for-beginners/
In this course, you will learn how to offer an end-to-end ML development process for designing, building, and managing the AI model lifetime.
- The Current State of AI
- MLOps helps overcome problems in AI deployment, including the AI Model Lifecycle.
- Introduction to ML Platforms.
Conclusion
Understanding MLOps is critical for assuring the stability, scalability, and efficiency of machine learning projects in production. The sources listed above provide a wealth of knowledge, practical examples, and necessary tools to assist you in understanding and effectively using MLOps ideas. Whether you’re a newbie just getting started or an experienced practitioner trying to expand your expertise, these materials will help you master MLOps.
