Understanding Deployment in the AI Lifecycle
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Course Modules:
Module 1: The AI Lifecycle Overview
Phases of AI development: problem definition, data, modeling, deployment, monitoring
Stakeholders and team roles in the lifecycle
How deployment connects experimentation to impact
Module 2: What Is Model Deployment?
Core concepts: inference, latency, scaling, APIs
Model packaging vs. serving
Deployment types: batch, real-time, edge
Module 3: Infrastructure and Environments
Local, on-premises, and cloud deployment
Overview of containerization (Docker)
Deployment targets: web apps, mobile, APIs, hardware
Module 4: Tools and Frameworks in Deployment
Flask, FastAPI, and RESTful services
MLOps tools: MLflow, DVC, TensorFlow Serving
CI/CD for AI workflows
Module 5: Monitoring and Lifecycle Management
Why monitoring models in production is essential
Detecting data drift and prediction errors
Updating and retraining deployed models
Module 6: Deployment Strategy and Governance
Model versioning and rollback
Security, privacy, and compliance
Aligning deployment with business goals
Module 7: Final Assessment & Scenario-Based Evaluation
Analyze a case study and recommend a deployment strategy
Identify weak points in an AI pipeline
Present a plan for real-world model lifecycle integration
Tools & Technologies Referenced:
Flask, Docker, GitHub
MLflow, FastAPI, Kubernetes (intro only)
Cloud platforms: AWS, GCP, Azure
Target Audience:
AI/ML students looking to understand deployment context
Product managers and analysts working with AI teams
Data scientists preparing for real-world collaboration
Technical professionals planning AI integration into apps
Global Learning Benefits:
Understand the critical role of deployment in AI success
Communicate effectively with developers and engineers
Align technical AI workflows with business delivery goals
Prepare to lead or contribute to end-to-end AI projects
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