Duration 3 days – 21 hrs
Overview
This advanced training course guides participants through the end-to-end process of deploying machine learning and AI models into production environments. Using modern tools such as Flask, FastAPI, Docker, and cloud platforms like AWS, Azure, or Google Cloud Platform (GCP), learners will package, containerize, and serve models through REST APIs, enabling real-world integration and scalability.
Objectives
- Build RESTful APIs to serve AI/ML models using Flask or FastAPI
- Containerize AI applications using Docker for portability and consistency
- Deploy and manage model services on cloud platforms (AWS, Azure, GCP)
- Monitor and update deployed models in production settings
- Understand key DevOps principles for AI operations (AI/ML Ops)
Audience
- Machine learning engineers, AI developers, and DevOps professionals
- Data scientists ready to transition from experimentation to deployment
- Software engineers integrating ML models into production environments
- Technical leaders building scalable AI pipelines
Prerequisites
- Proficiency in Python programming
- Solid understanding of machine learning model development and training
- Basic experience with REST APIs, Git, and command-line tools
- Familiarity with cloud platforms (AWS, Azure, or GCP) is helpful but not required
Course Content
Day 1: Building REST APIs for ML Models
- Introduction to model deployment workflows
- Serving models using Flask and FastAPI
- Input/output handling, model versioning, and validation
- Hands-on: Build and test a local REST API for a trained ML model
Day 2: Containerization with Docker
- Docker fundamentals: containers, images, Dockerfiles
- Creating Docker containers for AI applications
- Building production-ready containers with Flask/FastAPI apps
- Hands-on: Containerize your ML API and run locally
Day 3: Cloud Deployment and Best Practices
- Overview of AWS (EC2, Lambda, SageMaker), Azure ML, and GCP Vertex AI
- Deploying containers using AWS ECS/ECR, Azure Container Instances, or GCP Cloud Run
- Environment management, security, scalability, and monitoring
- Hands-on: Deploy your Dockerized model to a chosen cloud platform
- Final project: Full deployment pipeline from model to API to cloud
