Name: MLOps Fundamentals for Beginners – Learn DevOps for ML
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Created byRavi Kumar

Last updated 9/2025

English

What you'll learn

Understand the key concepts and benefits of MLOps in DevOps workflows.
Learn how to integrate machine learning models into CI/CD pipelines.
Gain hands-on experience deploying MLOps solutions using cloud platforms.
Master the tools and technologies required to transition from DevOps to MLOps.

Course content

6 sections • 22 lectures • 2h 44m total length

What is MLOps and Why is it Critical for DevOps Engineers?16:30
The Evolution: From DevOps to MLOps9:38
Understanding the MLOps Lifecycle9:55
Discover the MLOps lifecycle from data collection and preparation to model deployment, monitoring, and maintenance, including data cleaning, transformation, feature engineering, evaluation, governance and compliance, and continuous integration and deployment.
The Role of DevOps Engineers in MLOps and Machine Learning Pipelines6:17
Bridge development and operations by applying DevOps to the machine learning lifecycle. Implement continuous integration, delivery, automation, monitoring, and security to enable scalable ML model development, deployment, and maintenance.
MLOps Lifecycle

Core Concepts of Machine Learning (ML)4:51
Continuous Integration and Continuous Deployment (CI/CD) in DevOps7:11
Master DevOps to MLOps through continuous integration and delivery, detailing git-based code management, build pipelines with Jenkins, GitHub actions, and GitLab CI CD, and deployment to dev, staging, and production.
Tools for MLOps: Jenkins, Kubernetes, Docker, Git, Terraform4:29
Introduction to Cloud Platforms for MLOps (AWS, GCP, Azure)6:17

Building End-to-End Machine Learning7:43
Train an iris dataset model and deploy an end-to-end ML workflow on AWS, containerized with Docker, exposing a Python UI on port 5000 for predictions.
Train Your Machine Learning Model in Google Colab15:54
Managing ML Artifacts and Types of Model Artifacts in MLOPS World3:01
How to Select the ML Model in MLOPS World6:33
Define your problem, map data types, and select the right ML model—from structured data with XGBoost to unstructured data with CNN or Transformers.
ML Model Metrics Explanation Its Imporatance7:08
ML Model Metrics

What is Fast API Deploy Model with FAST API & Predict10:23
Learn how to deploy a trained ML model with FastAPI, expose real-time predictions via an API, and containerize the service with Docker for production-ready inference.
MLFLOW Deployment with Kubernetes and Helm20:36
ML Flow Set Up for Model Experiments3:36
Learn to set up MLflow for experiment tracking, configure a storage backend (MinIO or SQL databases), start the MLflow server, and prepare for logging experiments with Docker or Kubernetes.
Doing Experiment Tracking with MlFLow5:13

Requirements

Basic understanding of DevOps concepts and practices.
Familiarity with cloud platforms (AWS, GCP, or Azure) is beneficial but not required.
No prior experience in machine learning is needed; you will learn from scratch.

Description

Are you a DevOps Engineer, Cloud Professional, or AI Enthusiast looking to transition into the high-demand field of MLOps? This course is designed to help you bridge the gap between DevOps and AI Operations (AIOps) by equipping you with practical skills and real-world use cases.

In this course, you will:

Understand the evolution from DevOps to MLOps and why AI-driven workflows are the future.
Learn Kubernetes, Terraform, and CI/CD pipelines tailored for AI/ML model deployment.
Implement real-world projects on AWS, Azure, and GCP using Dockerized ML models.
Master end-to-end automation of Machine Learning pipelines with GitOps, ArgoCD, and Kubeflow.
Deploy AI models efficiently using feature stores, model registries, and cloud-native monitoring.

Who is this course for?

DevOps and Cloud Engineers looking to pivot into MLOps & AI Operations
Software Engineers eager to automate Machine Learning pipelines
Data Scientists interested in productionizing AI models
AI & ML professionals who want to scale deployments with Kubernetes and Terraform

What makes this course unique?

100% Hands-on Labs with real-world MLOps projects
Industry Best Practices from top tech companies
CI/CD Pipelines for AI/ML models using Terraform, Kubernetes, and Cloud services
Integrations with AWS SageMaker, Azure ML, and GCP AI

Join now and unlock the future of DevOps & MLOps careers!

Who this course is for:

DevOps engineers looking to transition into MLOps.
Cloud professionals interested in adding machine learning to their DevOps workflows.
AI enthusiasts who want to understand how MLOps bridges AI and DevOps.
Engineers or developers aiming to enhance their careers with in-demand MLOps skills.

What you'll learn

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Course content

Introduction to MLOps4 lectures • 42min

Setting Up the Foundations of MLOps4 lectures • 23min

Integrating Machine Learning By DevOps Engineer5 lectures • 40min

Deploying ML MODEL4 lectures • 40min

Advanced MLOps Practices and Best Practices2 lectures • 10min

The Future of MLOps and Career Advancement3 lectures • 9min

Requirements

Description

Who this course is for: