[Free] Machine Learning Operations (Mlops) For Ai Deployment
Learn MLOps, model deployment, CI/CD, automation, and production monitoring
What you’ll learn
- Design end-to-end MLOps pipelines covering data preparation, model training, evaluation, versioning, and experiment tracking.
- Apply version control, reproducibility, and collaboration practices to manage machine learning code, data, and models.
- Build automated machine learning workflows using CI/CD pipelines, automated testing, and pipeline orchestration.
- Deploy machine learning models using containerization and REST APIs, and distinguish between batch and real-time inference strategies.
- Monitor production ML systems for performance, data drift, and model drift, and identify appropriate retraining, tooling, and governance strategies.
Requirements
- Basic familiarity with Python programming and fundamental machine learning concepts is recommended
Description
This course is designed to equip students with a basic to advanced understanding of the concepts and practices of Machine Learning Operations (MLOps) for deploying AI systems in real-world environments. In this course, students will learn the machine learning lifecycle as a whole, from data ingestion, preprocessing, and versioning to model training, evaluation, and experiment tracking. The discussion continues with reproducibility and collaboration practices, including version control for code, data, and models, which shows how disciplined engineering workflows turn experimental models into reliable, maintainable systems.
Next, students will explore the automation of machine learning workflows, with a focus on continuous integration and continuous delivery (CI/CD) pipelines, automated testing, and pipeline orchestration. This course also covers model deployment strategies, including containerization, serving models through REST APIs, and batch versus real-time inference, as well as important issues such as performance monitoring, data drift, model drift, and retraining strategies. In the final sessions, students are introduced to the evaluation and selection of MLOps tools and cloud platforms, along with the governance, security, and ethical considerations of operating AI systems in production.
Through a combination of conceptual foundations and hands-on implementation, this course provides comprehensive skills for designing, building, deploying, and maintaining production-ready machine learning systems. By the end of the course, participants are expected to be able to design and implement end-to-end MLOps pipelines, apply CI/CD principles to machine learning projects, deploy and monitor models in production, and justify their choice of MLOps tools and platforms based on scalability, cost, and use case.
Author(s): Taman Belajar, Bina Nusantara University, Stefanus Benhard
