🧠 AI for Technical Program Manager
Master GenAI & Agentic AI with cloud-native architectures across AWS, Azure, and GCP. Design, deploy, and scale real-world AI systems with hands-on projects
AI
🎓 Duration: 6 Weeks
🧑🏫 Mode: Live Online | Hands-on + Projects
🧠 Program Overview:
This advanced AI training program equips participants with comprehensive knowledge of:
Generative AI and Agentic AI architectures
Core ML and DL concepts
Cloud-native AI infrastructure and deployment strategies
It emphasizes real-world applications across AWS, Azure, and GCP, focusing on scalability, cost optimization, governance, and MLOps.
📚 Modules Breakdown
Module 1: ML, DL & Cloud AI Fundamentals
ML lifecycle and key algorithms
DL essentials: CNNs, Transformers, embeddings
Intro to Generative & Agentic AI
Cloud AI ecosystems: AWS, Azure, GCP
Compute (GPUs, TPUs), storage, networking
Module 2: Generative AI Architectures in the Cloud
LLMs, diffusion models, VAEs
Multimodal inputs: text, image, audio
Cloud APIs: Bedrock, Vertex AI, Azure OpenAI
Fine-tuning vs. prompt engineering
Data labeling & cloud storage optimization
Module 3: Agentic AI Systems & Orchestration
Reasoning, planning, memory
Multi-agent orchestration: LangChain, AutoGen, CrewAI, Semantic Kernel
API, vector DB, cloud function integration
Distributed orchestration
Reliability & scalability design
Module 4: Inference Architecture & Cost Optimization
Hosted APIs, serverless endpoints, clusters
Autoscaling, load balancing, GPU pooling
Cost strategies: quantization, caching, speculative decoding
Cloud billing breakdown
Latency vs. cost trade-offs
Module 5: MLOps, LLMOps & Observability
Pipelines: Sagemaker, Vertex AI, Azure ML
Prompt management, model versioning, retraining
CI/CD for AI
Observability tools: CloudWatch, Stackdriver, Azure Monitor
Dashboards: performance, drift, safety
Module 6: Governance, Security & Compliance
IAM frameworks
Encryption, DLP, GDPR, HIPAA, SOC2
Responsible AI & bias mitigation
Red-teaming & prompt injection defense
Model cards & governance packs
Module 7: Scaling Agentic AI & Vendor Strategy
Multi-cloud & hybrid scaling
Latency management across regions
Vendor comparison: Bedrock, Azure OpenAI, Vertex AI
Resource scheduling & distributed inference
Cost-optimized cloud roadmap
🎓 Capstone Project
Design and deploy a production-grade Agentic AI system with:
Cloud integration and monitoring
Cost-performance alignment
Peer review and defense
Vendor strategy and scaling roadmap
🛠️ Hands-On Projects
ML pipeline on cloud notebooks (Sagemaker, Vertex AI, Azure ML)
AI Product Brief for generative/agentic use case
Generative AI architecture using cloud model API
ADR-001: Model & Cloud Data Strategy
Agentic AI workflow with orchestration layer (Lambda, Functions, Cloud Run)
ADR-002: Agent Architecture & Integration Plan
Inference architecture with cost optimization
ADR-003: Inference & Cost Efficiency
Monitoring dashboard for AI inference
ADR-004: Observability & Rollback Plan
Governance Pack: model card, DPIA, compliance checklist
Security risk assessment
Vendor & Cost Optimization Matrix
ADR-005: Cloud Scaling & Vendor Strategy
