DevOps to MLOps Bootcamp: Build & Deploy MLSystems End-2-End
What you'll learn
- Build end-to-end Machine Learning pipelines with MLOps best practices
- Understand and implement ML lifecycle from data engineering to model deployment
- Set up MLFlow for experiment tracking and model versioning
- Package and serve models using FastAPI and Docker
- Automate workflows using GitHub Actions for CI pipelines
- Deploy inference infrastructure on Kubernetes using KIND
- Use Streamlit for building lightweight ML web interfaces
- Learn GitOps-based CD pipelines using ArgoCD
- Serve models in production using Seldon Core
- Monitor models with Prometheus and Grafana for production insights
- Understand handoff workflows between Data Science, ML Engineering, and DevOps
- Build foundational skills to transition from DevOps to MLOps roles
Requirements
- Basic knowledge of DevOps and Docker
- Familiarity with Git and GitHub
- Some exposure to Python (used for scripting and ML workflows)
- Prior understanding of CI/CD concepts is helpful but not mandatory
- A machine with minimum 8GB RAM and Docker installed for running local labs
Description
This hands-on bootcamp is designed to help DevOps Engineers and infrastructure professionals transition into the growing field of MLOps. With AI/ML rapidly becoming an integral part of modern applications, MLOps has emerged as the critical bridge between machine learning models and production systems.
In this course, you will work on a real-world regression use case — predicting house prices — and take it all the way from data processing to production deployment on Kubernetes. You’ll start by setting up your environment using Docker and MLFlow for tracking experiments. You’ll understand the machine learning lifecycle and get hands-on experience with data engineering, feature engineering, and model experimentation using Jupyter notebooks.
Next, you'll package the model with FastAPI and deploy it alongside a Streamlit-based UI. You’ll write GitHub Actions workflows to automate your ML pipeline for CI and use DockerHub to push your model containers.
In the later stages, you'll build a scalable inference infrastructure using Kubernetes, expose services, and connect frontends and backends using service discovery. You’ll explore production-grade model serving with Seldon Core and monitor your deployments with Prometheus and Grafana dashboards.
Finally, you'll explore GitOps-based continuous delivery using ArgoCD to manage and deploy changes to your Kubernetes cluster in a clean and automated way.
By the end of this course, you'll be equipped with the knowledge and hands-on experience to operate and automate machine learning workflows using DevOps practices — making you job-ready for MLOps and AI Platform Engineering roles.
Who this course is for:
- DevOps Engineers looking to break into the field of MLOps
- Platform Engineers and SREs supporting ML teams
- Cloud Engineers wanting to understand ML workflows and productionization
- Developers transitioning into ML Engineering or Data Engineering roles
- Anyone curious about how real-world ML systems are deployed and scaled
Instructors
- 4.4 Instructor Rating
- 5,651 Reviews
- 159,212 Students
- 23 Courses
Helping DevOps Engineers Build Real-World Skills — One Project at a Time
Hi, I’m Gourav — Founder of School of DevOps™ and creator of the RealOps Career Framework.
With over 17 years of hands-on experience in DevOps, Cloud, and Platform Engineering, I’ve helped thousands of engineers go from confusion to confidence — and land roles they never thought possible.
Why Learn From Me?
* Real-World Projects, Not Just Theory
My courses are built around problems real companies solve every day — so you’re not just watching, you’re building.
* Designed for Career Impact
Whether you're transitioning into DevOps, going deeper with Kubernetes, or leveling up into MLOps/AIOps, my courses follow a clear roadmap toward mastery.
* Project-Based Learning Meets Gamification
I don’t believe in passive learning. My students build live systems, earn XP, showcase projects, and grow within a community of builders.
* Join 150,000+ Learners Across 15+ Courses
I’ve designed industry-ready bootcamps and Weekend Project Series that are beginner-friendly, yet deep enough for working professionals.
What I Teach
- DevOps Foundations + CI/CD
- Kubernetes, GitOps, Platform Engineering
- Cloud Infra with Terraform & AWS
- MLOps, AIOps & AI-Augmented Ops
- Agentic Workflows & AI for Infra Automation
Be Part of Something Bigger
After you finish this course, don’t stop there.
Join the RealOps Builders Network — our free Discord + Substack community for DevOps professionals who want to:
✅ Build projects every week
✅ Follow curated roadmaps
✅ Earn XP for real-world skills
✅ Get peer feedback and visibility
Join the network using the link in my bio — Let’s build your career, together.
- 4.4 Instructor Rating
- 2,785 Reviews
- 176,515 Students
- 69 Courses
Vivian Aranha is an experienced technology professional with a strong academic foundation and a passion for innovation in Artificial Intelligence. He earned his Bachelor’s degree in Information Technology in 2004, followed by a Master’s degree in Computer Science in 2006. Since then, Vivian has accumulated nearly two decades of experience across diverse roles in the tech industry, contributing to cutting-edge projects and technological advancements.
Over the past eight years, Vivian has been deeply involved in the field of Artificial Intelligence, working on impactful AI projects spanning machine learning, deep learning, and intelligent systems. His expertise extends beyond technical implementation, as he has also dedicated significant time to teaching and mentoring peers and aspiring AI professionals. Vivian combines his deep technical knowledge with a talent for simplifying complex concepts, empowering students and professionals to excel in the ever-evolving AI landscape.
With a commitment to continuous learning and knowledge-sharing, Vivian Aranha is not only building intelligent systems but also shaping the next generation of AI innovators.
- 4.3 Instructor Rating
- 5,634 Reviews
- 84,061 Students
- 22 Courses
Envisioned by Gourav Shah, a devops expert, author and a international trainer, School of Devops is a global provider of devops trainings with most comprehensive, job oriented, certification driven training programs. School of Devops also builds devops learning tools, technologies and top quality audio/visual content. Schoolofdevops developed it’s own Devops workspace called Codespace, an open scoure IDE. Codespace is based on it's front end engine Akurath, a terminal, a Devops workspace.
Being passionate about world of Open Source software, and later managing systems at scale, Gourav has transformed himself to be an expert Devops Enabler.
We have trained more than 8000 IT professionals from leading tech firms including CISCO, Intuit, Walmart Labs, Dreamworks, Accenture, Cognizant, Capgemini, RBS, Wells Fargo, Oracle etc.making us a coveted devops training company.