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Progress Tracking

This document tracks progress through the AI Architecture Master program.

Current Status

Phase: 0 - Setup ✅ Complete
Next Phase: 1 - AI Foundations
Focus: Neural networks, transformers, PyTorch basics

Phase 0: Setup ✅ Complete

Completed: February 2026

Task Status Notes
Repository structure created ✅ Done
README with provider-agnostic approach ✅ Done
PROJECT-INSTRUCTIONS.md updated ✅ Done
docs/ROADMAP.md updated ✅ Done
docs/DECISIONS.md updated ✅ Done
docs/PROGRESS.md updated ✅ Done
Development environment (Docker, VSCode, Python) ✅ Done
Ollama installed and tested ✅ Done llama3.2 working
AWS account configured ✅ Done
AWS budget alerts configured ($10, $25, $50) ✅ Done
AWS CLI installed and configured ✅ Done us-east-1
IAM user created (ai-master-cli) ✅ Done Bedrock access only
Bedrock model access verified ✅ Done Claude Haiku 4.5 tested

Phase 0 Deliverables

Phase 0 Completion Criteria

  • Can run ollama run llama3.2 locally
  • Can run aws bedrock list-foundation-models successfully
  • Docker environment working
  • All documentation updated and consistent

Phase 1: AI Foundations

Task Status Notes
Neural networks concepts ⬜ Not Started
Transformers / Attention mechanism ⬜ Not Started
PyTorch basics ⬜ Not Started
How LLMs work ⬜ Not Started
Project: Dockerized API with pre-trained model ⬜ Not Started

Phase 1 Completion Criteria

  • Can explain attention mechanism on a whiteboard
  • Can explain why LLMs hallucinate
  • Working API serving a HuggingFace model in Docker

Phase 2: LLMs + Provider Abstraction

Task Status Notes
LLMProvider interface design ⬜ Not Started
OllamaProvider implementation ⬜ Not Started
BedrockProvider implementation ⬜ Not Started
Prompt engineering patterns ⬜ Not Started
Streaming and error handling ⬜ Not Started
Project: Multi-provider chatbot ⬜ Not Started

Phase 2 Completion Criteria

  • Provider abstraction library working
  • Same chatbot code works with Ollama AND Bedrock
  • Can switch providers at runtime
  • Documentation comparing providers

Phase 3: RAG Systems

Task Status Notes
Embeddings concepts and models ⬜ Not Started
Chroma (local dev) ⬜ Not Started
pgvector setup ⬜ Not Started
Chunking strategies ⬜ Not Started
Hybrid search ⬜ Not Started
Evaluation pipeline ⬜ Not Started
Project: Documentation system with evaluation ⬜ Not Started

Phase 3 Completion Criteria

  • RAG system with swappable vector stores
  • Evaluation metrics implemented (relevance, groundedness)
  • Can explain chunking strategy trade-offs

Phase 4: Autonomous Agents

Task Status Notes
ReAct, Plan-and-Execute patterns ⬜ Not Started
Tool calling across providers ⬜ Not Started
LangGraph fundamentals ⬜ Not Started
Memory patterns ⬜ Not Started
Multi-agent systems ⬜ Not Started
Project: Business process automation agent ⬜ Not Started
Project: Multi-agent collaborative system ⬜ Not Started

Phase 4 Completion Criteria

  • Agent with multiple tools working
  • Human-in-the-loop implemented
  • Multi-agent system with supervisor pattern
  • Comparative doc: LangGraph vs Bedrock Agents

Phase 5: Infrastructure & MLOps

Task Status Notes
Docker best practices for AI ⬜ Not Started
ECS Fargate deployment ⬜ Not Started
Lambda for inference ⬜ Not Started
Terraform/CDK ⬜ Not Started
CI/CD with GitHub Actions ⬜ Not Started
Observability (CloudWatch, X-Ray) ⬜ Not Started
Project: Production API with observability ⬜ Not Started

Phase 5 Completion Criteria

  • Infrastructure as Code (Terraform or CDK)
  • CI/CD pipeline deploying to AWS
  • Monitoring dashboard with custom AI metrics
  • Cost tracking implemented

Phase 6: Enterprise Integrations

Task Status Notes
Integration patterns ⬜ Not Started
Authentication & security ⬜ Not Started
SAP AI Core / BTP ⬜ Not Started
SAP HANA Vector Engine ⬜ Not Started
S/4HANA APIs ⬜ Not Started
Project: Enterprise agent with SAP connectivity ⬜ Not Started

Phase 6 Completion Criteria

  • Working integration with SAP (or equivalent ERP)
  • Security and compliance checklist completed
  • Enterprise integration patterns documented

Phase 7: Consolidation

Task Status Notes
Fine-tuning (LoRA, QLoRA) ⬜ Not Started
Security & compliance deep dive ⬜ Not Started
Reference architecture document ⬜ Not Started
Portfolio polish ⬜ Not Started
Technical articles ⬜ Not Started

Phase 7 Completion Criteria

  • Reference Architecture Document published
  • Portfolio with 8-10 projects
  • At least 2 technical articles published
  • Clear positioning as AI Architect

Project Deliverables Summary

Phase Project Status
1 Dockerized API with pre-trained model ⬜ Not Started
2 Multi-provider chatbot ⬜ Not Started
3 Documentation system with evaluation ⬜ Not Started
4 Business process automation agent ⬜ Not Started
4 Multi-agent collaborative system ⬜ Not Started
5 Production API with observability ⬜ Not Started
6 Enterprise agent with SAP connectivity ⬜ Not Started
7 Reference Architecture Document ⬜ Not Started

Total Projects: 8

Monthly Budget Tracking

Month Spent Notes
Feb 2026 $0 Setup phase, local only
Mar 2026 -
Apr 2026 -

Budget Alerts:

  • $10 → Review consumption
  • $25 → Identify unnecessary resources
  • $50 → Shut down non-essential

Weekly Log

Week of Feb 4, 2026

  • Phase 0 Complete! 🎉
  • Bedrock access verified (Claude Haiku 4.5 tested in playground)
  • CLI access confirmed (aws bedrock list-foundation-models)
  • All documentation finalized (AWS-SETUP.md, OLLAMA-SETUP.md)
  • Ready to start Phase 1

Week of Feb 3, 2026

  • Repository restructured with provider-agnostic approach
  • Ollama installed and tested with llama3.2
  • AWS budget alerts configured ($10/$25/$50)
  • IAM user created (ai-master-cli) with Bedrock-only access
  • AWS CLI configured (us-east-1, json output)
  • AWS-SETUP.md documented (Sections 1-3, 5)
  • OLLAMA-SETUP.md created

Notes

  • Quality over speed - no deadlines, but consistent progress
  • Document failures and learnings, not just successes
  • Update this file weekly minimum

Last Updated: February 2026