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Artificial Intelligence

ML in Action: Hands-On Guide to Deploying and Serving Models

4 Modules20 Lessons100020 Learners

Learn how to deploy and serve machine learning models using APIs, Docker, cloud platforms, and production best practices for scalable, reliable, and real-world AI applications.

Start LearningLast Updated: 27th July, 2026

Course Curriculum

Module 1 Fundamentals of Model Deployment

What is Model Deployment?

Difference Between Training and Deployment

Deployment Challenges

What is Model Serving?

Synchronous vs Asynchronous Serving

Scaling Considerations

Module 2Deployment Architectures and Tools

Deployment Architectures

Popular Tools & Frameworks

Module 3Hands-On Model Deployment

Preparing Your Model

Building APIs for Model Serving

Containerization & Deployment

Module 4Best Practices, MLOps, and Real-World Tips

Version Control and Reproducibility

Monitoring and Logging

CI/CD for Machine Learning

Detecting Data Drift

Automating Retraining

Security and Compliance

Preparing for Edge Cases

Handling Traffic Spikes

Team Collaboration and Communication

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Summary

Written by Industry ExpertsSelf-paced Learning with Unlimited AccessEarn a Certificate Upon Compeletition

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