MLOps Course Online

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50 Hours

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igmGuru’s MLOps training program helps you master the key concepts of managing and deploying machine learning models in real-world environments. Our MLOps course is designed for both beginners and professionals, helping them understand how ML models work and learn how to deploy, manage, monitor, and scale them in real-world production environments. In this MLOps certification program, you will work with the same AI-driven tools used at companies like Google, Microsoft, and Amazon - Docker, Kubernetes, MLflow, Jenkins, and Azure Machine Learning - through hands-on labs and end-to-end projects. So, watch the first class for free and get started with MLOps.

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MLOps Course Overview

MLOps, or Machine Learning Operations, is the engineering discipline that connects data science with real-world production systems. This MLOps course gives you a structured, end-to-end understanding of how organizations build, deploy, and maintain machine learning models at scale.

Whether you are stepping into MLOps for the first time or looking to formalize skills you have already been applying on the job, this program meets you where you are. In this 50-hour MLOps online certification program, you will move through core concepts, hands-on tool work, and live project experience- everything needed to operate confidently in production ML environments where reliability, speed, and scalability are non-negotiable.

By the time you complete this MLOps certification program, you will not just understand the theory. You will have built actual pipelines, resolved real deployment challenges, and earned a credential that validates your readiness for production ML roles.

MLOps Students Also Learn

MLOps Fundamentals

MLflow

Python

Machine Learning

Prerequisite

Basic knowledge of Python programming
Familiarity with core machine learning concepts (training, testing, evaluation)
Basic understanding of the command line / Linux environment
Exposure to any cloud platform - AWS, Azure, or GCP (preferred, not mandatory)
Basic knowledge of Git and version control
No prior MLOps experience required

Skills You’ll Learn

Here are the key concepts you will learn during the training program.

Build a strong foundation in Python, core MLOps principles, and data management to develop and deploy machine learning models confidently in real production environments.
Work hands-on with industry-leading platforms, including Amazon SageMaker, AWS, Microsoft Azure, MLflow, and Hugging Face to build end-to-end ML solutions, create automated pipelines, and develop production-ready APIs.
Fine-tune and deploy Large Language Models (LLMs) using Hugging Face and package containerized models in the ONNX format for efficient, cross-platform deployment.
Design and implement a complete MLOps pipeline using MLflow- covering project structuring, model versioning, registry management, and experiment tracking from start to finish.

15+ Essential Skills Covered In This Program

✔️ Machine Learning Lifecycle Management

✔️ CI/CD for Machine Learning

✔️ Model Versioning

✔️ Experiment Tracking

✔️ Feature Engineering & Feature Stores

✔️ Data Pipeline Automation

✔️ Model Deployment (Batch, Real-time, Streaming)

✔️ Model Monitoring & Drift Detection

✔️ Containerization (Docker)

✔️ Container Orchestration (Kubernetes)

✔️ Workflow Orchestration (Airflow)

✔️ Cloud ML Platforms (AWS SageMaker, Azure ML, GCP Vertex AI)

✔️ Model Governance & Reproducibility

✔️ A/B Testing & Canary Deployments

✔️ Python for MLOps

15+ Tools Covered in This Course

This MLOps online course gives you hands-on experience with the specific MLOps tools that appear in job descriptions for MLOps engineers in 2026:

Version Control & Experiment Tracking: Git, DVC, MLflow
Containerization & Orchestration: Docker, Kubernetes, Kubeflow
CI/CD Automation: Jenkins, GitLab CI
Cloud Platforms: Azure Machine Learning, Amazon SageMaker, Google Cloud Vertex AI
Monitoring & Observability: Grafana, Prometheus
ML Frameworks: TensorFlow, Keras, Scikit-learn, PyTorch
Programming & Scripting: Python, Linux/Bash
Emerging Stack: Kubeflow, Hugging Face for production LLM pipelines
Feature Stores: Feast, Tecton
LLMOps Stack: LangChain, ONNX, Vector Databases
Governance & Explainability: SHAP, Model Cards, Policy-as-Code

Career Outcomes

The following are the high-paying roles you can choose after completing the training.

MLOps Engineer
Machine Learning Engineer
AI/ML Platform Engineer
ML Infrastructure Engineer
AI/ML DevOps Engineer
ML Reliability Engineer
LLMOps Engineer (emerging and high-demand in 2026)

Top Companies Actively Hiring MLOps Professionals

Global Tech Giants: Google, Microsoft, Amazon, Meta
Cloud & Data Platforms: Databricks, Snowflake, Salesforce, Oracle
AI Infrastructure & Tools: NVIDIA, Hugging Face, Weights & Biases, DataRobot
Enterprises & Fintech: JPMorgan Chase, Capital One, PayPal, Stripe

MLOps Engineer Salary

The demand for MLOps professionals has significantly outpaced the supply of qualified talent.

Experience Level	India (Annual Salary)	USA (Annual Salary)
Entry-Level (0-2 Years)	₹8 - ₹15 LPA	$82,000 - $110,000
Mid-Level (2-5 Years)	₹15 - ₹30 LPA	$110,000 - $150,000
Senior-Level (5-8 Years)	₹30 - ₹50 LPA	$150,000 - $180,000
Lead/Principal (8+ Years)	₹50 - ₹80+ LPA	$180,000 - $250,000+

Why Choose igmGuru for MLOps Course?

Experienced Trainers: Learn directly from industry veterans with hands-on experience at top organizations like Microsoft, IBM, and Google. Our trainers do not just teach concepts- they bring real production challenges and solutions into every session.
Alumni Working at Top MNCs: Our graduates are employed at leading multinational companies across the US, UK, and India. When you join igmGuru, you join a network of professionals who have already made the transition successfully.
Always Up-to-Date Curriculum: The MLOps landscape is constantly evolving. Our course outline is continuously updated to reflect the latest tools, frameworks, and industry practices- so what you learn today is what employers are looking for right now.
End-to-End Career Support: From resume building and LinkedIn optimization to interview preparation with the most commonly asked MLOps interview questions and placement assistance, igmGuru supports you well beyond the last session. The goal is not just certification- it is your next job offer.
Lifetime Access to Learning Material: All session recordings, course materials, and lab resources remain accessible to you indefinitely. Revisit any module, refresh your knowledge before an interview, or explore a tool again whenever you need to.

Sujit Nair

MLOps Trainer | Ex-IBM

Sujit is a seasoned MLOps Trainer specializing in ML pipeline automation, model deployment, CI/CD for machine learning, cloud-native ML infrastructure, and production monitoring. His training approach centers on hands-on labs, real-world deployment pipelines, and industry case studies, helping learners transition from "notebook to production" with confidence.

MLOps and ML Engineering professional with 15+ years of experience across data science, DevOps, and cloud infrastructure.
Successfully trained and mentored 1000+ learners and working professionals across MLOps, ML engineering, and AI infrastructure domains.
Expertise in CI/CD for ML, Model Versioning, Experiment Tracking, Feature Stores, Model Monitoring, Data Drift Detection, and end-to-end ML Pipeline Orchestration.
Hands-on experience with leading MLOps tools, including MLflow, Kubeflow, DVC, Airflow, Docker, Kubernetes, Jenkins, TensorFlow Extended (TFX), and Prometheus/Grafana.
Strong knowledge of deployment strategies, including Canary, Shadow, and Blue-Green deployments, along with model governance and reproducibility best practices.
Specialized in building and scaling ML pipelines across AWS (SageMaker), Microsoft Azure (Azure ML), and Google Cloud Platform (Vertex AI).
Helps learners prepare for industry-recognized MLOps certifications and real-world roles such as MLOps Engineer, ML Platform Engineer, and Applied ML Engineer.

Key Features

100% Money-Back Guarantee
Flexible Schedule
24 X 7 Lifetime Support
100% Job Assistance
Get Certified & Get Placed
Small Batches Upto 10 Participants

MLOps Training Modules

Lesson 1 - MLOps Introduction

1. What is MLOps & MLOps Motivation

2. Solutions and Future Trends

3. MLOps Components

4. Different Roles involved in MLOps ( ML Engineering + Operations )

5. Machine Learning Life Cycle

6. MLOps Vs DevOps

7. Major Phases — what it takes to master MLOps

8. Different tools for MLOps

9. MLOps Maturity Model Levels

10. MLOps - Stages Of CI / CD

Lesson 2 - Linux Basics Foundation

1. Why Linux? Linux types? How to access Linux env in different system

2. Free tier Amazon EC2 ubuntu instance

3. SSH and SSH tools & Putty

4. File zilla & WinSCP

5. Introduction to Shell , Bash Shell & Basic Linux Commands

6. Help for Command Line

7. Linux Core Concepts & Kernel and types

8. Linux file system, Boot Sequence, Run levels, File Types & Filesystem Hierarchy

9. Package Management Introduction and Configuration

10. Linux Type Based Package Manager

11. RPM and YUM

12. DPKG and APT

13. File Compression and Archival, Searching for Files and Patterns using grep/wildcards etc

14. VI, Nano Editor

15. Security and File Permissions, The Security Incident (story)

16. Linux Accounts, User Management, Access Control Files, Account Management

17. File Permissions and Ownership , Cron jobs

18. Service management with systemd, Working overtime (story)

19. Creating a Systemd Service, Systemd Tools

20. Lab - systemd services

21. Assignment | Assignment Solution

Lesson 3 - GIT Foundation - Github

1. What? Why? When? Type? Vendor? Pricing? Industry wise uses of GIT

2. Creation of Github / Gitlab / bitbucket account

3. Local GitHub UI installation, setup with VSCode and Pycharm

4. Local and Remote Repositories installation and configuration

5. GIT Repository initialisation

6. Commands: git log

7. Git Branches - What is branching in Git and why we need it?

8. Master/main branch and user-defined branch

9. Checkout and pushing to a branch, Merging of branches

10. Project control and management

11. In Remote Repositories, Initialisation of Remote Repositories

12. Pushing code to the remote repositories

13. Cloning of the remote repositories to local

14. PR (Pull Requests), Fetch and Pull

15. Handling conflict on merging branch, Forking of repository

16. Rebasing, Resetting and Reverting, Stashing

17. Assignment | Assignment Solution

Lesson 4 - Data version control DVC

1. What is DVC, DVC Uses, Installation in Mac OS, Windows & Linux

2. Data Versioning, Model Versioning

3. Data Access, Model Access & Data Pipelines, Metrics, Parameters, Plots

4. Run, Queue, Compare, Persisting, and Sharing Experiments

5. Clean up, Versioning Data and Models, Sharing Data and Model Files

6. Data Registries, Shared Development Server & Project Structure

7. Setup Google Drive Remote, Large Dataset Optimisation

8. External Dependencies, Managing External Data

9. Automate Pipelines with DVC, Pipelines & Experiment Automation, Build automated pipelines

10. Experiments Management, Experimenting with reproducible pipelines, Common issues with ML experiments

11. Tracking metrics and plots & Compare experiment results, Build, Test & Deploy

12. Introduction to CI/CD in Machine Learning & Build CI/CD pipeline

13. Install GitLab Runner and Trigger CI/CD pipeline

14. Build Machine Learning pipeline, Build CI/CD pipeline, Trigger CI/CD pipeline

15. Making Continuous Integration work with ML, DVC Integration with Project

16. Build a model Prototype, Build a prototype with Jupyter Notebook

17. Start to version your code with Git, Version your code with Git

18. Create pipelines, Automate pipelines and data versioning with DVC

19. Create CI pipeline to build, test, experiment, Experimenting with DVC and CML & Deploy your model

20. Assignment | Assignment Solution

Lesson 5 - DevOps - Docker Foundation

1. What is DevOps, Why DevOps

2. Dev-Test-Deploy ,DevOps Principles,DevOps Toolchain

3. Overview of DevOps Tools

4. Co-relation between Agile and DevOps,Categories of DevOps Tools

5. Containers Concepts , Container Vs Virtual Machine

6. Installing docker on CentOS, Debian and Windows

7. Managing Container with Docker Commands

8. Building your own docker images & Docker Compose

9. Docker registry - Docker Hub , Networking inside single docker container

10. Lab - Running Python Web App in docker container

11. Lab - Create a docker image from git repo

12. Lab - Deploying flask app using docker-compose

13. Lab - Complex deployment using docker-compose

14. Lab - Creating your own docker registry

15. Assignment | Assignment Solution

Lesson 6 - DevOps - Kubernetes Foundation

1. Introduction to Kubernetes

2. Architecture and Kubernetes cluster installation

3. Raft Consensus Algorithm and Networking in Kubernetes

4. Raft Consensus Algorithm and Networking in Kubernetes

5. Installing Minikube and Objects in Kubernetes - Pod, Deployment

6. Services - Service Discovery, Service Object, Headless Services, Service Type

7. Role based Access

8. Volumes - Persistent Volumes, Persistent Volume Claim, Storage Class

9. Config Map and Secrets

10. Ingress - Virtual Host, Types, Fanout, Virtual Host, Fanout Ingress configuration,

11. Virtual Host Ingress configuration

12. Lab - Installing Minikube on EC2

13. Lab - Enable and access Dashboard Addon

14. Lab - Deploy flask web app on Minikube

15. Lab - Deploy Nginx app on Minikube

16. Lab - Deploy application with host type volumes

17. Assignment | Assignment Solution

Lesson 7 - DevOps - Continuous Monitoring with Prometheus and Graffana

1. Introduction to Prometheus

2. Prometheus installation

3. Introduction to Grafana

4. Grafana Installation

5. Integration of Prometheus and Grafana

6. Adding customised dashboard in Grafana

7. Introduction to node exporter

8. Integrating node exporter for monitoring

9. Lab - Scrape metric from Grafana

10. Lab - View Node exporter metric in Grafana

11. Lab - View Docker metric in Grafana

12. Lab - Import AWS EC2 dashboard in Grafana

13. Assignment | Assignment Solution

Lesson 8 - DevOps - Continuous Integration using Jenkins

1. Introduction to Jenkins

2. Continuous Integration & Continuous Integration with Jenkins

3. Jenkins Architecture

4. Installing Jenkins on EC2

5. User management

6. Set up Jenkins Master & Slave

7. Setup CI-CD pipeline for sample project

8. Lab - Setup Role based access

9. Lab - Master/Slave Setup

10. Lab - Configure SCM in Jenkins

11. Assignment | Assignment Solution

Lesson 9 - MLFlow ( Manage your Machine Learning Lifecycle )

1. What is MLFLow & Installation

2. MLFlow Tracking, Where Runs Are Recorded, How Runs and Arti-facts are Recorded

3. Scenario 1: MLFlow on localhost

4. Scenario 2: MLFlow on localhost with SQLite

5. Scenario 3: MLFlow on localhost with Tracking Server

6. Scenario 4: MLFlow with remote Tracking Server, backend and arti-fact stores

7. Logging Data to Runs, Logging Functions, Launching Multiple Runs in One Program, Performance Tracking with Metrics

8. Visualising Metrics, Automatic Logging

9. Scikit-learn, TensorFlow and Keras, Gluon, XGBoost, Pytorch

10. MLFLow Tracker, Organising Runs in Experiments, Managing Experiments and Runs with the Tracking Service API, Tracking UI

11. Querying Runs Programmatically, MLFlow Tracking Servers, Storage,Networking

12. Logging to a Tracking Server, MLFlow Projects, Specifying Projects, Running Projects, Iterating Quickly, Building Multi Step Workflows

13. MLFLow Models, Storage Format, Model Signature And Input Example

14. Model API, Built-In Model Flavours, Model Customisation, Built-In Deployment Tools, Deployment to Custom Targets

15. Model Registry, Model Registry Workflows, UI Workflow, Registering a Model, Using the Model Registry, API Workflow

16. Adding an MLFlow Model to the Model Registry, Fetching an MLFlow Model from the Model Registry

17. Serving an MLFlow Model from Model Registry, Adding or Updating an MLFlow Model Descriptions, Renaming an MLFlow Model

18. Transitioning an MLFlow Model’s Stage, Listing and Searching MLFlow Models, Archiving an MLFlow Model, Deleting MLFlow Models

19. Assignment | Assignment Solution

Lesson 10 - Tensor Flow Extend (TFX)

1. Introduction to TFX

2. Data Ingestion using TFX & Data Validation using TFDV

3. Data Preprocessing using TFT

4. Model Training, Model Analysis & Model Evaluation using TFX

5. Model Deployment using TF Serving

6. Assignment Assignment Solution

Lesson 11 - KubeFlow ( Model Version Control & ML Pipeline )

1. What is Kubeflow?

2. Core Kubeflow components

3. How to set up Kubeflow on Kubernetes

4. How to develop basic ML models in Kubeflow Notebooks

5. How to train and deploy models in Kubeflow

6. How to use Kubeflow Pipelines

7. How to use KFServing to deploy models

8. How to manage logs with Kubeflow Metadata component

9. Katib Hyper Parameter Tuning

10. Kubeflow Pipelines to KFServing

11. Assignment | Assignment Solution

Lesson 12 - GitLab Foundation

1. GitLab Triggers

2. AWS S3 storage

3. GitLab CI/CD Pipelines

4. Pipelines definition

5. MongoDB cloud Atlas

6. Heroku | Logdata | Coral for Monitoring

7. Assignment | Assignment Solution

Lesson 13 - AWS MLOps - MLOps in Cloud

1. Amazon Sagemaker | Amazon S3 | AWS Codebuild | AWS Codecommit

2. Sagemaker Training Job | Sage Maker Endpoint | Amazon Api Gateway

3. Sagemake Model Monitoring | Cloudwatch Synthetics | Cloudwatch Alarm

4. Assignment | Assignment Solution

Lesson 14 - Azure MLOps - MLOps in Cloud

1. Create an Azure Machine Learning workspace

2. Setup a new project in Azure DevOps

3. Import existing YAML pipeline to Azure DevOps

4. Declare variables for CI/CD pipeline

5. Create training compute

6. Train ML model | Register model

7. Deploy model in AKS

8. Assignment | Assignment Solution

Lesson 15 - Projects

1. Deploy a Personalized Product Recommendation using MLOps

2. Deploy a Classification Model using MLOps on AWS

3. Deploy a Multiple Linear Regression Model using MLOps

4. Deploy a Gaussian Model in Time Series using MLOps on AWS

5. Deploy a Customer Churn Prediction using MLOps on Azure

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Want to know Today's Offer

MLOps Certification Exam

On successful completion of training, you receive an MLOps Certification by igmGuru. This is a verifiable credential you can add to your LinkedIn profile, resume, and professional portfolio. It signals to employers that you have not just studied MLOps in theory- you have built real systems and seen them through to deployment.

MLOps Certification Exam Guide 2026

If you are preparing for a specific vendor certification after this training, here is a practical breakdown of what each exam tests and how to approach it.

AWS ML Specialty Exam

The AWS ML Specialty exam covers four domains: data engineering, exploratory data analysis, modelling, and machine learning implementation and operations. The operations domain - which includes deployment, monitoring, and cost optimization - is where this course’s CI/CD and cloud modules will serve you most directly.

Azure AI Engineer Associate exam

The Azure AI Engineer Associate exam tests your ability to architect and implement AI solutions, including using Azure Machine Learning pipelines and managing deployed model endpoints. Expect scenario-based questions, not just recall.

Google Cloud Professional ML Engineer exam

The Google Cloud Professional ML Engineer exam is notably practical. It tests your ability to architect ML systems that are scalable, reproducible, and maintainable - the exact competencies this course builds across Modules 1 through 8.

None of these certifications requires you to attend official training before sitting the exam, though having real hands-on experience - which this course provides - is the most reliable preparation.

FAQ: MLOps Training

Q1. Who can enroll in this MLOps course?

Data scientists, ML engineers, software developers, and DevOps professionals looking to work with production ML systems.

Q2. Do I need prior machine learning experience?

Basic knowledge of Python and machine learning concepts is recommended before starting this course.

Q3. Will I get a certificate after completion?

Yes, you'll receive a course completion certificate after finishing all modules and assignments.

Q4. What tools will I learn in this course?

You'll work with MLflow, Docker, Kubernetes, Airflow, and cloud platforms like AWS, Azure, and GCP.

Q5. Is this course self-paced or instructor-led?

Both options are available- choose self-paced learning or live instructor-led sessions.

Q6. Will I work on real-world projects?

Yes, the course includes hands-on projects covering end-to-end ML pipeline deployment.

Q7. Does this course help with job placement?

Yes, you'll get placement assistance along with interview preparation support.

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Karthik Acharya

15-May-2026

MLOps Course

Recently, I completed MLOps training, and the team has provided me with a clear and practical understanding of deploying and managing machine learning models in production. The hands-on examples with tools like MLflow, Docker, and FastAPI make it highly useful for beginners and professionals alike.