Approach:
- Integrated Weekly Cycles: Rotate through all 4 topics every week to maintain breadth and depth simultaneously.
- Hands-on Projects & Review Loops: Each cycle includes learning, building, and reflection to ensure mastery.
- Focus Areas: Technical skills, leadership context, tooling, real-world application.
Weekly Learning Plan Structure (Repeatable & Cyclical)
| Day | Focus | Topics Rotated Each Week | Activities |
| Mon | DevOps Focus | Automation, CI/CD, Infra as Code, Security | Hands-on labs, tool config, read docs |
| Tue | MLOps Focus | Model deployment, pipelines, monitoring | Build pipelines, review SOTA tools |
| Wed | Agentic AI Focus | LLM agents, orchestration, fallbacks | Code agents, deploy locally, read papers |
| Thu | Developer Productivity Focus | DX tools, testing culture, code quality | Try productivity tools, improve workflows |
| Fri | CTO Integration Day | Cross-topic reflections, case studies | Write architectural notes, simulate scenarios |
| Weekend | Deep Dives (Optional/Rotational) | Pick 1-2 topics for extended practice | Hack projects, read books, build demos |
Detailed Checklist Per Topic
1️⃣ DevOps Mastery Checklist
Core Knowledge Areas
- ✅ CI/CD Pipelines
- Jenkins / GitHub Actions / GitLab CI / ArgoCD
- Blue-Green Deployments & Canary Releases
- ✅ Infrastructure as Code (IaC)
- Terraform / Pulumi / AWS CDK
- ✅ Containerization & Orchestration
- Docker, Kubernetes (EKS/GKE/AKS)
- Helm for Kubernetes deployments
- ✅ Cloud Architecture
- AWS / GCP / Azure Fundamentals
- Serverless (Lambda, API Gateway) vs Kubernetes trade-offs
- ✅ Security & Compliance
- SAST/DAST Integration in CI/CD
- Secret Management (Vault, SSM Parameter Store)
- Role-based Access Control (RBAC)
Hands-on Regular Practice
- Build pipelines for at least 2 sample projects.
- Setup multi-region K8s cluster with observability.
- Simulate security breaches & implement mitigation.
Leadership Integration
- Prepare DevOps architecture pitch decks.
- Review cost optimizations for cloud infra.
- Implement DevOps OKRs for teams.
2️⃣ MLOps Mastery Checklist
Core Knowledge Areas
- ✅ Model Lifecycle Management
- Versioning: DVC / MLflow / Weights & Biases
- Experiment tracking and reproducibility
- ✅ Model Deployment & Serving
- FastAPI / TorchServe / NVIDIA Triton
- Serverless ML vs Containerized ML
- ✅ Pipeline Orchestration
- Kubeflow / Airflow / Metaflow / Prefect
- ✅ Monitoring & Retraining
- Drift Detection (Evidently AI, Alibi Detect)
- Continuous Training loops
- ✅ DataOps Integration
- Data Validation (Great Expectations, TFX Data Validation)
- Data Lake / Data Warehouse Setup
Hands-on Regular Practice
- Build an end-to-end MLOps pipeline weekly (small model).
- Automate model retraining with synthetic drift.
- Deploy models via REST and streaming endpoints.
Leadership Integration
- Define MLOps best practices and policies.
- Create guidelines for ethical AI deployment.
- Align MLOps with product timelines and SLAs.
3️⃣ Agentic AI Mastery Checklist
Core Knowledge Areas
- ✅ LLM Orchestration Frameworks
- LangChain / CrewAI / OpenDevin / AutoGen
- Tool use, fallback loops, reflection
- ✅ Actionable Memory & Context Management
- Vector DBs (Pinecone, Weaviate, Milvus)
- Memory replay, episodic vs semantic memory
- ✅ Autonomy & Workflow Integration
- Human-in-the-loop design patterns
- Function calling (OpenAI, Anthropic, etc.)
- ✅ Security & Governance in Agentic AI
- Guardrails (Guardrails AI / Rebuff / PromptLayer)
- Prompt injection protection
- ✅ Emerging Research
- Review latest papers from arXiv, OpenAI, Anthropic, Meta AI
- Follow AutoGPT, Devin, BabyAGI evolution
Hands-on Regular Practice
- Build a personal agent that uses web search & RAG.
- Create fallback loops with LLM failure handling.
- Deploy a small agent to Slack or web interface.
Leadership Integration
- Define safe deployment frameworks for agents.
- Create ROI models for AI autonomy in your product.
- Evaluate agent observability and debugging needs.
4️⃣ Developer Productivity Mastery Checklist
Core Knowledge Areas
- ✅ Development Environment Optimization
- Remote Dev Environments (Codespaces, DevContainers)
- Dotfile and tool standardization across teams
- ✅ Testing & QA Automation
- Contract Testing (Pact.io), E2E (Cypress / Playwright)
- Shift-left security & quality practices
- ✅ Code Quality & Review Process
- Linting, Static Analysis (SonarQube, CodeQL)
- Review culture and merge workflow design
- ✅ Observability for Developers
- Error tracking (Sentry), Performance Monitoring (Datadog, NewRelic)
- Feature flag management (LaunchDarkly)
- ✅ Team Productivity Metrics
- DORA Metrics (Deployment Frequency, Lead Time)
- Cognitive Load reduction strategies (Team Topologies)
Hands-on Regular Practice
- Implement feature flag deployment for a test project.
- Set up automated testing with pipelines weekly.
- Track team productivity using DORA in a sandbox repo.
Leadership Integration
- Design the “Golden Path” for new developers.
- Evaluate dev team feedback loops and retrospectives.
- Introduce gamification for productivity metrics (optional).
Cross-Discipline Leadership Habits
| Action | Frequency |
| Write & present architectural notes | Weekly |
| Conduct mock boardroom Q&As | Bi-weekly |
| Mentor team in one focus area | Weekly |
| Run blameless post-mortems | Monthly |
| Engage with startup CTO community | Weekly |
Tools & Resources
Learning Platforms
- KubeAcademy, Coursera, DeepLearning.AI, MLOps Community
- PapersWithCode, arXiv Sanity, AI Agent Hackathons
- LeadDev, CTO Craft, DORA DevOps Reports
Simulated Projects to Cycle Through
- Deploy an ML model via CI/CD to a cloud-native API (DevOps + MLOps)
- Build an AI agent that assists with code reviews (Agentic AI + DevProd)
- Create a full-stack monitoring pipeline with real-time alerts (DevOps + DevProd)
Optional: Visual Tracking (Kanban or OKRs)
Track progress via:
- GitHub Projects / Linear / Notion Kanban
- Bi-weekly OKRs for each category
Also Read: Models – What are they?