An MLOps engineer takes machine-learning models from a notebook to production and keeps them healthy: data and training pipelines, model serving, monitoring for drift, and automated retraining.
Open any step to see its lessons, tools and lab, and mark it as learning or done. Your progress is saved in this browser only.
Write clean, working Python programs
Build a command-line tool that cleans and summarises CSV data
MLOps Engineering: Notebook to Production · Generative AI & RAG Engineering · AI Agents with LLMs
Collaborate on code with Git like a professional team
Run a team pull-request workflow end to end
MLOps Engineering: Notebook to Production · DevSecOps: Secure CI/CD & Supply Chain · Getting Started with Cloud Automation
Work confidently on Linux servers from the command line
Automate a web server setup with a Bash script
DevSecOps: Secure CI/CD & Supply Chain · SRE & Observability · Getting Started with Cloud Automation
Build and evaluate machine learning models
Customer churn prediction model
MLOps Engineering: Notebook to Production · Azure MLOps Engineer (AI-300) · Data Science Fundamentals
Package and run any app in containers
Containerise a three-service application
MLOps Engineering: Notebook to Production · DevSecOps: Secure CI/CD & Supply Chain · Forward Deployed Engineer Program
Build reliable CI/CD pipelines
Pipeline that tests, builds and deploys on every push
MLOps Engineering: Notebook to Production · DevSecOps: Secure CI/CD & Supply Chain · Automation Testing with AI: Playwright & API
Take models from notebook to production
Train-to-serve pipeline with automatic retraining
MLOps Engineering: Notebook to Production · Azure MLOps Engineer (AI-300) · AWS AI & Machine Learning (MLA-C02)
Deploy and run applications on Kubernetes
Deploy a production-style app with Helm
MLOps Engineering: Notebook to Production · DevSecOps: Secure CI/CD & Supply Chain · Platform Engineering with Kubernetes & Terraform
Build reliable data pipelines at scale
Batch and streaming pipeline into a lakehouse
Run LLM applications reliably in production
Evaluation harness that scores every release
MLOps Engineering: Notebook to Production · LLMOps & AI Observability · Azure MLOps Engineer (AI-300)
Run reliable systems with SRE practices
SLO dashboard with burn-rate alerts
LLMOps & AI Observability · DevSecOps: Secure CI/CD & Supply Chain · Platform Engineering with Kubernetes & Terraform
Present your skills confidently to employers
Mock technical + HR interview with feedback
MLOps Engineering: Notebook to Production · Generative AI & RAG Engineering · DevSecOps: Secure CI/CD & Supply Chain
A practical, step-by-step plan. Skip any step you already know.
3 steps · about 28 hours
3 steps · about 36 hours
3 steps · about 44 hours
3 steps · about 26 hours
Use the hands-on labs in each step, publish your work on GitHub and write a short README for each project. Projects are what interviewers ask about first.
Practise the questions asked for this role, do mock interviews and polish your resume and LinkedIn profile. Practise MLOps Engineer interview questions →
Start with roles that match your current level, keep learning on the job, and grow towards the senior skills in this path.
Indicative yearly pay, in lakh rupees (LPA). Pay varies with city, company, skills and certifications, so use these as a guide, not a promise.
Rounded ranges from public salary surveys (AmbitionBox, Glassdoor and PayScale India), checked November 2026. Bangalore, Hyderabad and Pune often pay above these ranges, and product companies and global capability centres pay more than service companies.
DevOps ships application code. MLOps also has to version data and models, track experiments, and watch model quality after release.
LLMOps applies MLOps ideas to large language model apps: prompt and model versioning, evaluation, guardrails, cost and latency monitoring.
Our advisors will match the path to the right SOTT course for your background.
