About mlops-for-devops

MLOps for DevOps Engineers - A hands-on, project-based guide to Machine Learning Operations

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README.md

MLOps for DevOps Engineers

A hands-on, project-based guide to Machine Learning Operations built specifically for DevOps, Platform, and SRE engineers.

No ML background required. Every concept is explained through DevOps analogies you already understand.

If you are completely new to MLOps, read our DevOps to MLOps guide first.

Who This Is For

Most MLOps resources are written for data scientists learning infrastructure. This repo flips that.

You do not need to become a data scientist. But just like understanding how a Java application is built makes you a better DevOps engineer, understanding how an ML model is built, trained, and served makes you effective at operating ML workloads in production.

What We Build

Track	What You Learn
🤖 Traditional ML	Train, serve, automate, and monitor a real ML model on Kubernetes
🧠 Foundational Models	Serve LLMs in production using vLLM, TGI, and Ollama
⚙️ LLM-Powered DevOps	Monitor K8s clusters, build RAG pipelines and agents with LLMs

Everything runs on Kubernetes, Docker, and tools you already use.

Prerequisites

Skill	Level
Kubernetes	Intermediate
AWS EKS	Working knowledge
Python	Basic (read and run scripts)

No ML experience needed. That is what this repo teaches.

Phase 1: Local Development & Data Pipelines (Start Here)

Goal: Build the required ML foundation for by building an Employee attrition prediction model from your local systems.

Use case throughout: Employee attrition prediction for a large organisation (~500,000 employees). One problem, end to end. Keeps the focus on infrastructure and operations, not data science theory.

Step	Title	Guide
1	Project Dataset Pipeline	Read the Guide
2	Data Preparation Stages	Read the Guide
3	Training & Building the Prediction Model	Read the Guide
4	From Model to Live API with KServe	Read the Guide

Code: phase-1-local-dev/

Phase 2: Enterprise Orchestration for ML

Goal: Replace local, manual ML workflows with production-grade orchestration. Versioned data, automated pipelines, experiment tracking, and scalable training.

Step	Title	Guide
1	Data Versioning Fundamentals	Read the Guide
2	Data Version Control (DVC) with AWS S3	Read the Guide
3	Data Versioning using Airflow on Kubernetes	Read The Guide
4	Feature Store Fundamentals Explained	Read The Guide
5	Hands-on Feature Store with Feast on Kubernetes	Read The Guide
6	Kubeflow Explained for MLOps	🔜 Coming Next
7	Hands-on Kubeflow on Kubernetes	🔜 Planned
8	MLflow Explained for MLOps	🔜 Planned

Code: phase-2-enterprise-setup/

Learning Path

Phase	Track	Title	Status
1	🤖 Traditional ML	Local Dev & Pipelines	✅ Done
1	🤖 Traditional ML	K8s Deploy & Model Serving	✅ Done
3	🤖 Traditional ML	Enterprise Orchestration	🔄 In Progress
4	🤖 Traditional ML	Monitor & Observe	🔜 Planned
5	🧠 Foundational Models	Foundational Models	🔜 Planned
6	🧠 Foundational Models	LLM Serving & Scaling	🔜 Planned
7	⚙️ LLM-Powered DevOps	LLM-Powered DevOps	🔜 Planned
8	⚙️ LLM-Powered DevOps	Emerging AI Ops	🔜 Planned

Tech Stack

Here is the tech stack you will be using in this setup.

Category	Tools
Data Pipeline	Python, Airflow
Model Training	scikit-learn
API / Serving	FastAPI, Flask, Docker, KServe
ML Orchestration	Kubeflow, MLflow Pipelines
Monitoring	Prometheus, Grafana, Evidently AI
Infrastructure	Kubernetes, Helm, GitHub Actions

Certifications

MLOps Tools

Ray: Open-source distributed computing framework For Python & AI Workloads
rtk: High-performance CLI proxy that reduces LLM token consumption.
CML: CI/CD for Machine Learning Projects

License

Dual licensed:

Code (scripts, configs, manifests) — Apache 2.0

For commercial licensing: [email protected]

mlops-for-devops