Monthly Plan

This file turns the 12-month roadmap into weekly execution.

Month 1: ML Fundamentals

Week 1

• Review linear algebra basics.
• Review probability and statistics basics.
• Create a sales prediction project skeleton.
• Build a naive baseline.

Week 2

• Implement data cleaning.
• Implement feature engineering.
• Train linear and tree-based models.
• Write initial evaluation notes.

Week 3

• Add cross-validation.
• Compare model metrics.
• Create feature importance analysis.
• Add leakage checks.

Week 4

• Build prediction API.
• Add Dockerfile.
• Complete README and evaluation report.
• Answer module assessment.

Month 2: Deep Learning

• Week 1: neural network basics and toy implementation.
• Week 2: train classifier and track experiments.
• Week 3: regularization, debugging, and model export.
• Week 4: inference API, Docker, documentation, assessment.

Month 3: Transformers and LLMs

• Week 1: tokenization, embeddings, attention.
• Week 2: implement attention and tiny transformer block.
• Week 3: hosted LLM integration and structured outputs.
• Week 4: tool calling, cost comparison, documentation, assessment.

Month 4: RAG Engineering

• Week 1: parsing, cleaning, chunking.
• Week 2: embeddings, vector search, metadata.
• Week 3: hybrid search, reranking, citations.
• Week 4: access control, evaluation, documentation, assessment.

Month 5: AI Agents

• Week 1: tool calling and schema validation.
• Week 2: agent loop, planning, state, and memory.
• Week 3: authorization, confirmation, audit logs.
• Week 4: evaluation, failure handling, documentation, assessment.

Month 6: AI Evaluation and LLMOps

• Week 1: golden datasets and evaluation schemas.
• Week 2: RAG and agent evaluators.
• Week 3: model comparison, cost, latency, token monitoring.
• Week 4: regression reports and release gates.

Month 7: Docker and Kubernetes

• Week 1: Dockerfiles and Compose.
• Week 2: multi-service local stack.
• Week 3: Kubernetes deployments, services, ingress, secrets.
• Week 4: health checks, scaling, resource limits, documentation.

Month 8: Cloud Architecture

• Week 1: compute, storage, and database architecture.
• Week 2: networking and identity.
• Week 3: observability, reliability, cost estimation.
• Week 4: architecture diagrams and ADRs.

Month 9: Security and IaC

• Week 1: threat modeling and least privilege.
• Week 2: AI security and prompt injection testing.
• Week 3: Terraform basics and modules.
• Week 4: environments, secrets, disaster recovery plan.

Month 10: AI Product and Transformation

• Week 1: product discovery and current-state mapping.
• Week 2: opportunity analysis and prioritization.
• Week 3: KPIs, ROI, and risk register.
• Week 4: final case study and roadmap.

Month 11: Enterprise AI Project

• Week 1: requirements, architecture, and data model.
• Week 2: RAG and agent vertical slice.
• Week 3: security, evaluation, and observability.
• Week 4: deployment, documentation, and demo.

Month 12: Portfolio and Final Review

• Week 1: audit all READMEs and diagrams.
• Week 2: complete missing evaluation reports and ADRs.
• Week 3: certification review or weak-area review.
• Week 4: final retrospective and skill matrix update.