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Product ID: OC-2025-B23A3
Operationalizing ML Models: MLOps for Scalable AI
Master MLOps to deploy, scale, and maintain ML models efficiently. Learn to build scalable workflows, set up CI/CD pipelines, monitor performance, and optimize AI infrastructure for real-world impact!
In partnership with
Overview
Ready to take your machine learning models from concept to real-world impact? This course is your gateway to mastering MLOps (Machine Learning Operations)—the essential skill set for deploying, scaling, and maintaining machine learning models effectively in production.
MLOps is all about ensuring your models run smoothly and efficiently, solving real-world challenges along the way. You’ll learn how to tackle performance issues before they escalate, create automated pipelines that make updates seamless, and establish best practices for managing AI infrastructure.
Skills you'll gain
MLOpsKubernetesCI/ CD pipelines Machine Learning Deployment MLflow
What you'll learn
- Implement scalable MLOps workflows that ensure efficient and reliable machine learning operations.
- Build CI/CD pipelines for seamless and automated model updates, streamlining the development lifecycle.
- Monitor deployed ML models for performance and drift.
- Optimize AI infrastructure to handle scalability challenges and support high-performance deployments.
Who Should Attend
Prerequisites
This course is perfect for Data Scientists, Machine Learning Engineers, AI Practitioners, and IT Professionals managing AI systems. Whether you're building models, deploying them in production, or overseeing AI infrastructure, mastering MLOps will equip you with the essential skills to streamline operations, ensure model performance, and optimize workflows. You'll gain hands-on experience in managing end-to-end machine learning pipelines, handling real-world challenges, and ensuring scalability and efficiency across AI systems.
To get the most out of this course, you should have a basic understanding of machine learning concepts, familiarity with Python programming, and some experience with Docker and containerization. These prerequisites will provide a solid foundation, allowing you to focus on the advanced MLOps techniques covered in the course. If you're comfortable with these areas, you'll be well-equipped to dive into real-world applications of MLOps and enhance your machine learning workflows.
Chapters
Explore a structured set of chapters designed to build your skills step by step, with practical examples and hands-on applications.
Meet your instructors
Luca Berton
View my channelFrequently Asked Questions
How much do the courses at Starweaver cost?
We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.
Do you offer any certifications upon completion of a course at Starweaver?
Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.
Does Starweaver offer any free courses or trials?
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Product ID: OC-2025-B23A3
NEW
4 hours 15 minutes of video content
Downloadable resources
Certificate of completion
Desktop and Mobile access
Luca Berton