A comprehensive 30-day journey through Advanced DevOps, MLOps, Platform Engineering, and AI Platform Integration.
✅ Week 1 – Advanced DevOps
✅ Week 2 – MLOps Engineering
✅ Week 3 – Platform Engineering
✅ Week 4 – AI Platform Integration
- Git Internals & Object Model
- Branching Strategies (Git Flow, Trunk-Based, GitHub Flow)
- Rebase vs Merge (Advanced Usage)
- Interactive Rebase & History Rewriting
- Cherry-Pick, Revert & Reset Strategies
- Git Bisect for Debugging
- Git Hooks (Client & Server Side)
- Git Submodules & Monorepo Strategy
- Semantic Versioning (SemVer)
- Tagging & Release Management
- Secure Git Workflows
- CI/CD Architecture & Pipeline Design
- Pipeline as Code
- Multi-Stage Pipeline Design
- Matrix Builds & Parallel Execution
- Reusable Workflows & Composite Actions
- Environment-Based Deployments (Dev/Staging/Prod)
- Secrets Management in CI/CD
- Dependency Caching Strategies
- Artifact Management
- Docker Build & Push Automation
- Infrastructure Deployment Automation
- Automated Versioning & Release Pipelines
- Blue-Green & Canary Deployment Automation
- Rollback Strategies
- CI/CD Security Best Practices
- Observability in CI/CD
- Containerization Fundamentals
- Dockerfile Best Practices
- Multi-Stage Builds
- Image Layer Optimization
- Distroless & Minimal Base Images
- Non-Root Containers
- Health Checks in Containers
- Container Networking Basics
- Docker Compose for Multi-Service Apps
- Container Image Scanning
- Software Bill of Materials (SBOM)
- Image Signing & Verification
- Secure Registry Practices
- Runtime Container Security
- Kubernetes Architecture Overview
- Control Plane Components
- Worker Node Components
- Pod Lifecycle
- Deployments & ReplicaSets
- Services & Service Types
- ConfigMaps & Secrets
- Resource Requests & Limits
- Liveness & Readiness Probes
- Horizontal Pod Autoscaler (HPA)
- Rolling Updates & Rollbacks
- Namespaces & Multi-Tenancy
- RBAC & Access Control
- Persistent Volumes & Storage Classes
- StatefulSets vs Deployments
- Kubernetes Networking Basics
- Policy as Code in Kubernetes (OPA/Gatekeeper Basics)
- Network Policies & Pod Security Standards
- Observability Principles
- Metrics vs Logs vs Traces
- Golden Signals
- Prometheus Architecture
- Metrics Instrumentation
- Grafana Dashboards
- Centralized Logging
- Log Aggregation Concepts
- Distributed Tracing Basics
- OpenTelemetry Fundamentals
- Alerting & Incident Response
- Monitoring Kubernetes Workloads
- SLOs, SLAs & SLIs
- DORA Metrics (Deployment Frequency, Lead Time, MTTR, Change Failure Rate)
- Error Budgets & SRE Principles
Build a production-grade CI/CD pipeline with GitHub Actions that includes: multi-stage Docker builds, image scanning (Trivy), OCI image signing (Cosign), push to registry, Kubernetes deployment via Helm, and Prometheus + Grafana observability stack — all triggered on PR merge.
- Machine Learning Lifecycle Overview
- Problem Framing & Business Understanding
- Data Collection & Data Versioning
- Data Preprocessing Pipelines
- Feature Engineering Fundamentals
- Training & Validation Strategies
- Model Evaluation Metrics
- Overfitting & Underfitting Concepts
- Experiment Tracking Concepts
- Reproducibility in ML
- Model Artifact Management
- ML Metadata Management
- MLflow Tracking & Registry
- DVC for Data Version Control
- Model Versioning Strategies
- Model Serialization Techniques
- Model Packaging Strategies
- REST API for ML Inference
- Batch vs Real-Time Inference
- Synchronous vs Asynchronous Serving
- FastAPI for Model Serving
- BentoML Fundamentals
- Model Registry Concepts
- Containerizing ML Models
- Health & Readiness Endpoints for Models
- Versioned Model Deployment
- API Performance Optimization
- GPU vs CPU Serving Considerations
- Scaling ML Inference Services
- GPU Infrastructure Basics (CUDA, NVIDIA Device Plugin for Kubernetes)
- Node Selectors & Tolerations for GPU Workloads
- Multi-Instance GPU (MIG) Concepts
- ML Pipeline Architecture
- Training Pipeline Automation
- Continuous Training (CT)
- Continuous Integration for ML
- Continuous Deployment for ML Models
- Model Testing Strategies
- Data Validation in Pipelines
- Automated Model Evaluation
- Model Promotion Strategies (Staging → Production)
- Feature Pipeline Automation
- Trigger-Based Retraining
- Model Drift Detection Concepts
- Automated Model Rollbacks
- GitOps for ML Deployments
- Data Quality Validation Concepts
- Schema Validation
- Data Drift Detection
- Concept Drift Fundamentals
- Great Expectations Framework
- Feature Store Fundamentals
- Online vs Offline Feature Stores
- Monitoring Model Performance in Production
- Logging ML Predictions
- Model Explainability Basics
- Responsible AI Concepts
- Alerting on ML Degradation
- Feedback Loops in ML Systems
- LLM Ops Introduction
- Prompt Versioning & Management
- LLM Observability & Tracing (LangSmith, Langfuse, Phoenix)
- Token Cost Tracking & Budgeting
- Output Validation & Guardrails
- Hallucination Detection Strategies
- LLM Evaluation Frameworks (RAGAS, DeepEval)
Build an end-to-end MLOps pipeline: train a model tracked in MLflow, package it as a FastAPI service containerized with Docker, set up DVC for data versioning, automate CI with GitHub Actions (lint, test, build, push), and deploy to Kubernetes with automated drift alerting via Grafana.
- Infrastructure as Code Principles
- Declarative vs Imperative Infrastructure
- Terraform Architecture Overview
- Providers & Resources
- Terraform State Management
- Remote Backends & State Locking
- Terraform Modules & Reusability
- Variable & Output Management
- Workspaces for Environment Isolation
- Dependency Management in Terraform
- DRY Infrastructure Patterns
- Provisioners & When to Avoid Them
- Terraform Plan & Apply Workflow
- Infrastructure Versioning Strategy
- Managing Multi-Environment Infrastructure
- Terraform Security Best Practices
- Crossplane: Kubernetes-Native Infrastructure Provisioning
- Comparing Terraform vs Crossplane vs Pulumi
- FinOps & Cloud Cost Management
- Cloud Cost Visibility & Tagging Strategies
- Rightsizing Compute Resources
- Cost Allocation with Kubernetes (Kubecost Concepts)
- Reserved Instances & Savings Plans
- Budget Alerts & Cost Anomaly Detection
- GitOps Principles
- Declarative Infrastructure & Deployments
- ArgoCD Architecture
- Helm Chart Fundamentals
- Helm Templating & Values Management
- Kustomize Basics
- Application Deployment via GitOps
- Automated Sync & Self-Healing
- GitOps Repository Structure Design
- Environment Promotion Strategy
- Drift Detection & Reconciliation
- Secret Management in GitOps
- Rollback Strategies in GitOps
- Multi-Cluster Deployment Patterns
- Observability in GitOps Workflows
- Flux CD as an Alternative to ArgoCD
- Progressive Delivery with Argo Rollouts
- Platform Engineering Fundamentals
- DevOps vs Platform Engineering
- Internal Developer Platform (IDP) Concepts
- Golden Path Strategy
- Self-Service Infrastructure
- Developer Experience (DevEx) Principles
- Backstage Architecture Overview
- Service Templates & Scaffolding
- CI/CD Template Automation
- Infrastructure Template Automation
- Kubernetes Resource Templates
- Policy as Code Concepts
- Standardization & Governance
- Platform API Design Concepts
- Scaling Engineering Teams with IDP
- Measuring Platform Success: DORA Metrics & Platform KPIs
- Service Mesh Fundamentals (Istio / Linkerd)
- Sidecar Proxy Architecture
- mTLS Between Services
- Traffic Management & Observability via Service Mesh
- Developer Portal & API Catalog Design
Design and deploy a mini Internal Developer Platform: Terraform modules for multi-environment infra (dev/staging/prod), ArgoCD managing application deployments via GitOps, Backstage service catalog with a working software template, and Crossplane provisioning a cloud resource on demand — all governed by OPA policies.
- Cloud-Native AI System Architecture
- Microservices Architecture for ML Systems
- ML Training → Validation → Deployment Flow
- Event-Driven Architecture for ML Pipelines
- API Gateway & Traffic Management
- Service Mesh Fundamentals
- Scalable Inference Architecture
- Batch vs Real-Time ML Architecture
- Model Registry Integration Patterns
- Data Pipeline Integration
- CI/CD + MLOps Integration Architecture
- GitOps-Driven ML Deployments
- Infrastructure + Application Layer Integration
- Multi-Environment Architecture Design
- High Availability & Fault Tolerance Design
- Performance Optimization Strategies
- RAG (Retrieval-Augmented Generation) Architecture
- Vector Database Concepts (Pinecone, Weaviate, Qdrant, pgvector)
- Embedding Pipeline Design
- Chunking Strategies for Documents
- Hybrid Search (Keyword + Semantic)
- RAG Evaluation & Quality Metrics
- Serving RAG Pipelines in Production
- LLM Infrastructure Patterns
- Self-Hosted vs Managed LLM APIs
- vLLM & TGI for High-Throughput Serving
- LLM Gateway & Rate Limiting
- Prompt Caching Strategies
- Multi-Model Routing Architecture
- Blue-Green Deployment Strategy
- Canary Deployment Strategy
- Progressive Delivery Concepts
- Traffic Splitting Techniques
- Feature Flags for ML Systems
- Model A/B Testing Strategy
- Shadow Deployment
- Automated Rollback Mechanisms
- Zero-Downtime Deployment Design
- Scaling Strategies (Horizontal & Vertical)
- Load Testing Concepts
- Chaos Engineering Basics
- Deployment Observability
- GPU-Aware Autoscaling (KEDA + GPU Metrics)
- LLM-Specific Deployment Patterns
- Token-per-Second Latency Targets
- Batching Strategies for Inference (Dynamic Batching)
- Quantization & Model Compression Concepts (INT8, FP16, GGUF)
- DevSecOps Principles
- Secure CI/CD Pipelines
- Container Security Hardening
- Kubernetes Security Best Practices
- RBAC Design Patterns
- Network Policies
- Secret Management Strategies
- Vault Concepts
- Policy as Code (OPA Concepts)
- Image Scanning & Vulnerability Management
- Supply Chain Security
- SBOM in Production Systems
- Compliance & Audit Logging
- Access Control & Identity Management
- Zero Trust Architecture Concepts
- AI-Specific Governance
- Model Access Control & API Key Management
- PII Detection & Data Redaction in LLM Pipelines
- AI Audit Trails & Compliance Logging
- Responsible AI Policies & Model Cards
- Input/Output Filtering & Content Moderation
- Technical Documentation Standards
- Architecture Decision Records (ADR)
- System Design Diagrams
- CI/CD Flow Documentation
- ML Pipeline Documentation
- Infrastructure Architecture Diagrams
- Deployment Flow Diagrams
- Security Architecture Documentation
- Monitoring & Alerting Documentation
- API Documentation Standards
- README Structure for Engineering Projects
- Operational Runbooks
- RAG & LLM System Architecture Documentation
- C4 Model for Software Architecture Diagrams
- Resume Structuring for AI Infrastructure Roles
- Highlighting DevOps + MLOps Experience
- Positioning as Platform Engineer
- Showcasing Architecture Projects
- Writing Technical Project Summaries
- Building a Strong GitHub Portfolio
- LinkedIn Optimization Strategy
- Preparing Impact-Based Project Descriptions
- Salary Negotiation Preparation
- Positioning for AI Platform / ML Infrastructure Roles
- Open Source Contributions as a Portfolio Signal
- DevOps Scenario-Based Questions
- MLOps Architecture Interview Questions
- Platform Engineering Case Studies
- Kubernetes Deep-Dive Questions
- CI/CD Troubleshooting Scenarios
- ML Production Failure Scenarios
- System Design for Scalable ML Platform
- Designing Multi-Tenant Platform Systems
- Incident Response Scenarios
- Trade-Off Analysis in Architecture
- Performance Bottleneck Debugging
- Mock Interview Simulation Topics
- LLM System Design Interview Patterns
- RAG System Design & Evaluation Questions
- FinOps & Cost Optimization Scenarios
Deploy a production-ready AI Platform end-to-end: A RAG-powered Q&A service backed by a vector database, served via a FastAPI + vLLM stack, containerized and deployed to Kubernetes with ArgoCD, monitored via OpenTelemetry + Grafana, secured with OPA policies and Vault secret management, and tracked end-to-end with LLM observability (Langfuse). Cost dashboards via Kubecost, full CI/CD pipeline, and a Backstage catalog entry — ready to demo in interviews.
Upon completion of this 30-day intensive program, you will have:
- Advanced DevOps Skills: Production-grade CI/CD, container security, and Kubernetes expertise
- MLOps Proficiency: Complete ML lifecycle management from experiment to production
- Platform Engineering Excellence: Infrastructure as Code, GitOps, and internal developer platforms
- AI Platform Integration: End-to-end AI infrastructure design, RAG systems, and LLM deployment strategies
- GPU & Inference Engineering: GPU-aware Kubernetes scheduling, model optimization, and high-throughput serving
- FinOps Awareness: Cloud cost visibility, rightsizing, and budget governance
- Security & Governance: DevSecOps, AI-specific compliance, and zero-trust architecture
- Career Readiness: Professional positioning, portfolio development, and interview preparation
| Week | Capstone Project |
|---|---|
| Week 1 | Production CI/CD pipeline with security scanning, image signing & Kubernetes observability |
| Week 2 | End-to-end MLOps pipeline with MLflow, FastAPI serving, DVC & drift alerting |
| Week 3 | Mini Internal Developer Platform with Terraform, ArgoCD, Backstage & OPA |
| Week 4 (Final) | Production RAG-powered AI platform on Kubernetes with full observability, security & CI/CD |
- Basic understanding of software development and deployment concepts
- Familiarity with cloud computing fundamentals
- Experience with at least one programming language (Python preferred for MLOps weeks)
- Basic knowledge of Linux/Unix systems
- Enthusiasm for learning cutting-edge DevOps and AI infrastructure technologies
| Category | Tools |
|---|---|
| CI/CD | GitHub Actions, GitLab CI |
| Containers | Docker, Podman, Buildah |
| Orchestration | Kubernetes, Helm, Kustomize |
| GitOps | ArgoCD, Flux CD |
| IaC | Terraform, Crossplane |
| MLOps | MLflow, DVC, BentoML, FastAPI |
| LLM Serving | vLLM, TGI, Ollama |
| LLM Observability | Langfuse, LangSmith, Phoenix |
| Vector DBs | Qdrant, Weaviate, pgvector |
| Observability | Prometheus, Grafana, OpenTelemetry, Jaeger |
| Security | Trivy, Cosign, OPA/Gatekeeper, HashiCorp Vault |
| FinOps | Kubecost, AWS Cost Explorer |
| IDP | Backstage |
- Clone this repository to your local machine
- Follow the weekly schedule systematically
- Complete hands-on exercises for each topic
- Build the weekly capstone projects to grow your portfolio
- Join the community for collaborative learning
If you find this repository helpful and plan to use it for learning, please consider giving it a star ⭐. Your support motivates me to keep improving and adding more valuable content! 🚀
This project is crafted with passion by Harshhaa 💡.
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