Online Course free

AI Model deployment on AWS

In today’s fast-paced machine learning landscape, building a great model is only half the battle—getting it into production quickly, reliably, and securely is where real impact happens. This course is designed to teach you exactly that.

You’ll learn how to deploy machine learning models on AWS, using real-world tools and workflows tailored for both small-scale and enterprise-grade applications. We’ll cover a range of deployment options—from lightweight and serverless setups with AWS Lambda, to managed ML services like Amazon SageMaker, containerized solutions with ECS + Fargate, and large-scale infrastructure using Amazon EKS and EC2.

But this course doesn’t stop at “how to deploy.” You’ll also learn:

• When and why to choose a specific service (e.g., latency-sensitive APIs vs. batch processing)
• How to manage traffic using blue/green, canary, and shadow deployments
• How to ensure scalability with autoscaling policies and serverless concurrency
• How to monitor, secure, and roll back deployments effectively
• How to integrate deployments into CI/CD pipelines for seamless automation

Through case studies, hands-on walkthroughs, and deployment strategy deep-dives, you’ll develop the skills to move models into production with confidence—whether you're building a quick prototype, scaling a SaaS platform, or working in a high-stakes regulated industry.

By the end of this course, you'll be able to:

• Select the most appropriate AWS service for your deployment needs
• Implement scalable, cost-effective, and secure ML inference endpoints
• Apply progressive rollout strategies to mitigate risk
• Automate and monitor deployments in a cloud-native, production-grade environment