Course Description

Module 1: AI Fundamentals (Week 1)

1. AI Basics: AI vs ML, Narrow vs General AI, Supervised/Unsupervised/Reinforcement Learning, enterprise use cases
2. AI in Business: Fortune 500 applications, ROI, challenges, build vs buy, ethics and bias
3. AWS AI Landscape: Pre-built vs custom ML, service categories, costs

Module 2: Amazon Bedrock (Week 2)

1. Introduction: Bedrock overview, models (Claude, Llama, Titan), security, compliance
2. Applications: Text generation, summarization, customer service, document processing, code generation
3. Hands-On: Setup, prompt engineering, cost optimization, integrations

Module 3: Amazon SageMaker (Week 3)

1. Overview: Studio & Canvas, pre-built models, training and deployment
2. Canvas: Data prep, visualization, training, evaluation, predictions
3. Enterprise Workflow: Model lifecycle, validation, deployment, monitoring, continuous improvement

Module 4: AWS AI Services for Business (Week 4)

1. Document Processing: Textract, Comprehend, Kendra
2. Customer Experience: Lex, Polly, Translate, Personalize
3. Visual AI: Rekognition, Lookout for Vision

Module 5: Enterprise AI Architecture (Week 5)

1. Patterns: Serverless AI, real-time vs batch, data lakes, governance
2. Integration: APIs, pipelines, hybrid cloud
3. Security & Compliance: Data privacy, GDPR, access control, audit

Module 6: AI Cost and ROI (Week 6)

1. Costs: Pricing models, compute/storage/transfer, budgeting
2. Optimization: Right-sizing, spot instances, model optimization, alerts
3. ROI: Metrics, cost-benefit, business impact, reporting

Module 7: Real-World AI (Week 7)

1. Industry Use Cases: Banking, Healthcare, Retail, Manufacturing
2. Case Studies: Successes, failures, best practices
3. AI Strategy: Readiness, prioritization, team building, governance
4. Assignment: Develop AI strategy for chosen industry

Module 8: Future-Proofing AI (Week 8)

1. Trends: Generative AI, Edge AI, IoT, multimodal, AI agents
2. Scaling: MLOps, pipelines, model versioning, A/B testing, performance optimization
3. Governance & Ethics: Responsible AI, bias detection, explainability, compliance
4. Capstone: Present AI implementation plan