AI Tools, Platforms & Deployment

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Course Modules:

Module 1: Introduction to AI Deployment

The AI lifecycle: from training to production

What is MLOps and why it matters

Key challenges in operationalizing AI

Module 2: Tools and Environments

Setting up Jupyter, Google Colab, and VSCode

Introduction to GitHub, Docker, and Conda environments

Local vs. cloud workflows

Module 3: Building APIs for AI Models

Using Flask or FastAPI to serve models

Creating REST endpoints for prediction

JSON request/response structure

Module 4: Containerization with Docker

Dockerizing your AI application

Building and running containers

Deploying containers locally or on the cloud

Module 5: Deploying on Cloud Platforms

Overview of AWS, Azure, and Google Cloud for AI

Deploying with Google Cloud Run / AWS Lambda

Using AI services (SageMaker, Vertex AI, etc.)

Module 6: Monitoring and Model Management

Logging and monitoring predictions

Versioning and retraining models

Data drift detection and alerts

Module 7: Automating Workflows with MLOps

CI/CD pipelines for ML

Using MLflow or DVC for experiment tracking

Scheduling with Airflow or Prefect

Module 8: Final Project – Deploy a Full AI Pipeline

Choose one:

Deploy a sentiment analysis API

Create a computer vision model served as a web app

Launch a forecasting model to the cloud

Submit Docker container, code repo, and live URL

Tools & Technologies Used:

Flask / FastAPI

Docker

Git, GitHub

AWS, Google Cloud, Azure

MLflow, DVC, Airflow (optional)

Target Audience:

Intermediate AI/ML practitioners

Developers who want to bring AI into production

Data scientists aiming for full-stack AI roles

Engineers automating intelligent systems

Global Learning Benefits:

Learn to turn AI models into production-ready applications

Master modern tools for model deployment and automation

Build a portfolio of deployable AI projects

Understand how to scale, manage, and maintain AI systems over time

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