Name: Ultimate DevOps to MLOps Bootcamp - Build ML CI/CD Pipelines
Rating: 4.5 (345 reviews)

Udemy Business

Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Created byGourav J. Shah, School of Devops

Last updated 8/2025

English

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

Course content

10 sections • 98 lectures • 11h 33m total length

Understand the MLOps Project you will Build in the Course7:05
Join RealOps Builders Community on Discord0:04

What is MLOps ?20:21
Story of Evolution of MLOps, LLMOps and AgenticAIOps16:38
Comparing Three Approaches to AI22:28
MLOps Case Studies – Learning from the Pioneers12:01
Comparing Devops vs MLOps20:58
Emergence of MLOps Engineer14:29
Explore the emergence of ml ops engineers and two streams: ml ops practitioners and ai platform engineers, driving data, model production, and scalable ml pipelines.
Facing the Production Gap
Who Does MLOps Anyway?
From MLOps to Agentic AI

Module Intro3:19
Use Case - House Price Predictor - Regression7:05
Fork and Clone the Repository0:06
Understanding End to End ML Practices and MLOps19:29
Environment Setup Overview9:23
Setting up Docker / Podman with Compose4:39
Launching MLflow for Experiemnt Tracking7:31
Understanding the Project Directory and Scaffold7:45
Setting up Python Virtual Environment with UV6:08
Working with Jupyter Notebooks6:54
Download the Lab Guide0:02
Ready to Roll with Your MLOps Environment
Summary3:48

Module Intro3:21
Explore how devops and mlops teams collaborate with data scientists and data engineers to understand applications, notebooks, and workflows, then build ml ci/cd pipelines with feature engineering and experimentation.
Learning Data Engineering12:53
Experimental Data Analysis8:08
Understaing Feature Engineering Concepts5:51
Building New Features for House Price Predictor5:24
Preparing for Model Experimentation6:18
Data Splitting with x_train, y_train, x_test, y_test4:57
Split data into x_train, y_train, x_test, and y_test from a csv to train and validate a model, using an 80/20 split of features and target with random_state.
Defining Algorithms and Hyperparameter Grids5:34
Running Model Experiments to find the Best Model and Hyperparamters9:23
Run a hyperparameter grid across linear regression, gradient boosting, XGBoost, and random forest to compare performance, then select gradient boosting as the best model with optimized configurations tracked in mlflow.
Download the Lab Guide0:01
From Raw Data to Trained Model
MLOps Foundations Review: From Idea to Model
Module Summary3:28

Module Intro0:44
Linear Regression5:03
Logistic Regression3:13
Decision Tree4:23
Explore how the decision tree algorithm works for on-the-spot loan decisions. See income, debt-to-income ratio, and credit score map to a simple, explainable flowchart.
Random Forest4:50
Support Vector Machine (SVM)2:29
Neural Networking5:01
Boosting Algorithms (XGBoost, LightGBM etc.)7:53
Explore boosting algorithms like gradient boosting, XGBoost, and LightGBM through an IPL player auction price example, showing how ensemble trees combine performance data to improve predictions in MLOps pipelines.
Match the Model! Core ML Algorithm Review
Module Summary1:24

Module Intro3:19
Handover from Data Scientist to ML Engineer / MLOps6:35
Running Feature Engineering and Preprocessing Jobs4:40
Building and Training Final Model with Configs from Data Scientists6:03
Wrapping the Model with FastAPI with Streamlit Client Apps6:47
Writing Dockerfile to package Model with FastAPI Wrapper17:46
Debugging and Fixing Image Failures, Launch and Validate FastAPI11:21
Packaging and testing Streamlit App9:29
Packaging and Model Serving Infra with Docker Compose14:52
Integrate dockerfiles for fast API and Streamlit with Docker Compose to launch interconnected containers, configure environment variables, and test end-to-end ML serving in a reproducible dev environment.
Download the Lab Guide0:01
Ship It! Packaging ML Models with Docker
Summary4:21

Moule Intro2:57
DAGs, GitHub Actions and our MLOps CI Workflow12:39
Understanding GitHub Actions Syntax7:38
Understand GitHub actions syntax to build a single MLOps CI workflow with events, runners, jobs, and steps. Map stages like data processing, feature engineering, model training, and publishing.
Writing an executung out first GitHub Actions Workflow13:50
Adding Data and Feature Engineering Steps with Model Training5:52
Model Training Step with MLFlow for Tracking7:42
Adding Image Build and Publish Step with Docker5:52
Add container image build and publish steps using a docker build action in GitHub Actions, including login, build context, dockerfile path, and tagging with the commit hash for Docker Hub.
Configurating Registry Token and publishing Image to DockerHub6:50
Modular, Multi Stage MLOps CI Workflow Pipeline14:15
Download the Lab Guide0:01
Automating MLOps with GitHub Actions
Summary2:38

Module Intro1:59
Designing Scalable Infrastructure for Model Inference4:34
Introduction to Kubernetes for Machine Learning10:28
Kubernetes Core Concepts - Pods, Deployments and Services9:12
Simplest way to build a 3 Node Kubernetes Cluster with KIND10:03
Deploying Streamlit Frontent App with Kubernetes12:04
Exposing the Streamlit App with Kubernetes NodePort Service6:24
Creating Deployment Service for the Model wrapped in FastAPI7:10
Connecting Streamlit with Model using Kubernetes native DNS Based Service Discov11:09
Easy way to Generate Kubernetes Manifets and YAML6:29
Download the Lab Guide0:01
Scaling ML Inference with Kubernetes
Summary1:57

Module Intro1:19
Explore auto scaling of machine learning models on Kubernetes with horizontal pod autoscaler, vertical pod autoscaler, and Keda, and set up Prometheus and Grafana for monitoring.
Project Spec2:35
Installing Prometheus and Grafana with Helm6:04
Deploy a kube-prometheus-stack monitoring system for your kubernetes environment with helm, exposing Grafana and Prometheus via node ports, using Artifact Hub presets with dashboards.
Exploring Monitoring Metrics with Grafana and Prometheus8:16
Adding Instrumentation for FastAPI along with Custom Dashboard11:27
Automatic Capacity Scaling Concepts4:01
Installing KEDA and Configuring Resource Spec5:21
Configuring Scaled Objects with KEDA7:25
Getting started with Load Testing Model Inference7:28
AI Based Troubleshooting Monitoring with ChatGPT8:52
Running Load Test and Analysing Autoscaling9:36
CPU Based Auto Scaling with KEDA10:44
Adding a Verticle Pod Autoscaler (VPA)11:41
Lab - Setting up Monitoring with Prometheus and Grafana0:01
Lab - Setting up ML Autoscaling0:01
Summary2:14

Module Intro0:39
GitOps Concepts4:56
GitOps Pricinple 2 : Start Revision Controling the Code6:05
GitOps Principle 4 : Setup a Agent - ArgoCD4:04
Enable Argo CD as a GitOps continuous deployment platform in your Kubernetes cluster. Create the Argo CD namespace, deploy manifests, and access the web UI to configure GitOps applications syncing.
Overview of Argo Application CRD4:55
Continuous Delivery with ArgoCD Applications12:00
End to End CI and CD Pielines for ML App23:37
Summary2:52

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

What you'll learn

Explore related topics

Course content

About this Course2 lectures • 7min

Conceptual Introduction to MLOps9 lectures • 1hr 47min

Use Case and Environment Setup13 lectures • 1hr 16min

From Data to Models - Understanding Data Science with Feature Engineering and Ex13 lectures • 1hr 5min

Bonus: Understanding the Core ML Algorithms10 lectures • 35min

Packaging Model along with FastAPI Wrapper and Streamlit with Containers12 lectures • 1hr 25min

Setting up MLOps CI Workflow with GitHub Actions12 lectures • 1hr 20min

Building Scalable Prod Inference Infrastructure with Kubernetes13 lectures • 1hr 22min

Monitoring and Autoscaling a ML Model16 lectures • 1hr 37min

GitOps Based Deployments for ML/LLM Apps8 lectures • 59min

Requirements

Description

Who this course is for: