Model Deployment and Serving

Learn to deploy and serve ML models efficiently. This course covers deployment strategies, cloud vs. on-premises trade-offs, and CI/CD best practices, equipping you with the skills to build scalable, reliable, and production-ready ML solutions.
What you'll learn

Deploying machine learning models is a critical step in the AI lifecycle, yet it presents unique challenges that differ from traditional software deployment. In this course, Model Deployment and Serving, you’ll learn to effectively deploy, serve, and manage machine learning models in production environments. First, you’ll explore the fundamental differences between model deployment and traditional software deployment, along with various strategies such as one-off, batch, real-time, and edge-based serving. Next, you’ll dive into model serving architectures and compare different approaches, including cloud-based, on-premises, serverless, and containerized deployments. Finally, you’ll gain hands-on experience by implementing a basic model deployment using a cloud platform like AWS SageMaker and setting up CI/CD pipelines for scalable and automated ML model delivery. When you’re finished with this course, you’ll have the skills and knowledge needed to confidently deploy machine learning models, optimize their serving performance, and implement robust monitoring and alerting mechanisms to ensure reliability in production environments.

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