Operationalizing ML Models: MLOps for Scalable AI is your gateway to mastering the deployment and maintenance of machine learning systems in real-world environments. Created by the Starweaver Instructor Team and taught by Luca Berton, this Coursera course dives deep into the tools, workflows, and best practices required to build resilient AI infrastructure.
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What You’ll Learn
- ✅ Design and implement scalable MLOps workflows
- ✅ Build and manage CI/CD pipelines for ML model deployment
- ✅ Monitor deployed models for drift and performance issues
- ✅ Optimize infrastructure for production-grade AI systems
Who Should Take This Course?
This course is ideal for:
- Machine Learning Engineers
- Data Scientists
- AI/ML Practitioners
- IT Professionals working on AI deployments
Prior experience with Python, ML basics, and Docker/containerization is recommended to get the most from this course.
Course Overview
- Introduction to MLOps and Operational Challenges
- CI/CD Pipelines for Machine Learning
- Monitoring and Observability in ML Deployments
- Handling Drift, Failures, and Model Lifecycle
- Scaling ML Systems Using Docker and Kubernetes
- Real-World Case Studies from Netflix, Uber, and Google
- Assignment: Deploying a Scalable ML Model with CI/CD
- Final Thoughts and Continued Learning Path
Certificate
Earn a Coursera shareable certificate to showcase your skills in scalable AI operations. Perfect for LinkedIn, resumes, or performance reviews.
Recently updated: March 2025 – Included with Coursera Plus.
Assignments & Discussions
- Assignment: Deploy and Monitor a Machine Learning Model
- Discussion: Lessons from Real-World MLOps Failures
- Discussion: Tools You Use for Model Monitoring and Drift Detection
About the Instructor
Luca Berton brings a practical approach to deploying ML systems. With expertise in DevOps, automation, and infrastructure management, Luca makes complex topics accessible to professionals of all levels.
Ready to turn your ML prototypes into production-ready systems?
Join now and operationalize your AI like the pros.
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