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MLOps Certification Course

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MLOps Certification Course

Name: MLOps Certification Course
Item: MLOps Certification Course
Rating: 4.5
Author: Dheerendra Yadav

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Edureka's online MLOps Certification Course will help you prepare for ML for Ops Architect, MLOps Engineer, MLOps Lead, etc. It covers the MLOps lifecycle, tools like MLflow and SageMaker, best practices for CI/CD, cloud platforms (AWS, Azure, GCP), cross-team collaboration, and real-world projects for hands-on deployment experience. You will learn how to apply DevOps principles to machine learning, enabling the smooth deployment and maintenance of ML models in production.

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Why should you take Machine Learning Operations Certification Course?

Global MLOps market is expected to reach $75.42 billion by 2033 from $2.08 billion in 2024 at a CAGR of 43.2% - Market.us

Large enterprises dominate the MLOps market, possessing a significant 71% share in the year 2023 - Market.us

The average salary for MLOps Engineer is $1,76,248 per year in the United States with a $38,892 cash bonus per year - Glassdoor

Curriculum

MLOps Essentials

Topics:

Introduction to MLOps
MLOps vs. DevOps
SDLC Basics
Waterfall vs AGILE vs DevOps vs MLOps
MLOps Phases
Versioning
Testing
Automation
Reproductibility
Deployment
Monitoring
MLOPs Architecture
ML Pipeline
MLOps Tools
MLOPs Case Study

Hands-on:

MLOps Case Study

Skills You will Learn

Understanding MLOps Concepts
Proficiency in SDLC Methodologies

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Version Control System

Topics:

Git Essentials
Configuring Git
Branching
Git Workflow
Repo
Git Commands
GitHub Action

Hands-on:

Git Common Commands
Branching and Merging

Skills You will Learn

Tracking and managing changes to code
Source Code Management
Tracking and Saving Changes in Files

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Packaging ML Models

Topics:

Model Packaging
Experimentation
Model Fitting
Challenges in Working inside the Jupyter Notebook
Create Config Module
Data Handling Module
Data Preprocessing Data Preprocessing
Pipelines
Training and Prediction
Requirements.txt file
Testing Virtual Environments
Pytest
Model Packaging and Testing

Hands-on:

ML Model Packaging and Testing

Skills You will Learn

Model Packaging Techniques
Data Preprocessing and Handling s
Testing and Validation of ML models

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Build MLApps using New Age Tools

Topics:

API Essentials
Streamlit Fundamentals
Working with Flask
REST API
FAST API
Building ML Model with Streamlit
Creating ML Model with Flask
ML Model Deployment with FAST API

Hands-on:

Developing ML models using Streamlit
Building ML models using Flask
Deploying ML models using FAST API

Skills You will Learn

ML Application Development
ML Pipelines with FAST API

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CI/CD for ML Models

Topics:

Introduction to CI/CD
CI/CD Challenges
CI/CD Implementation in ML
Popular DevOps Tools
AWS CodeCommit
AWS CodePipeline
AWS CodeBuild
AWS CodeDeploy
Azure Boards
Azure Repos
Azure Pipeline
Azure Test Plans
Azure Artifacts

Hands-on:

Building CI/CD Pipelines

Skills You will Learn

Implementation of CI/CD Pipelines for ML

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Machine Learning Model Management

Topics:

What is Model Management?
Activities in Model Management
Data Versioning
Code Versioning
Experiment Tracking
Model Cataloging
Model Monitoring
Overview of Model Management tools
Working with MLFlow

Hands-on:

Working with MLFlow

Skills You will Learn

Data Management
Code Versioning
Experiment Tracking

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Docker and Kubernetes for ML Deployment

Topics:

Containerization
Docker
Docker Architecture
Docker for Machine Learning
Docker Desktop Installation
Working with Docker
Running Docker Container
Dockerfile
Pushing Docker Image to DockerHub
Dockerize the ML Model
Container Orchestration
Kubernetes Core Concepts
Pod
Deployment
Replica
Service
Volumes (PVC)
Monitoring
Liveness and Readiness Probes
Labels and Selectors

Hands-on:

Docker CLI Commands
Writing a Dockerfile Deployments
DaemonSets
Deploying Services

Skills You will Learn

Continuous Deployment
Writing a Dockerfile to Create an Image
Installing Docker Compose
Configuring Local Registry
Container Orchestration
Application Deployment

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Monitoring and Debugging of ML System

Topics:

Model Monitoring Importance
Tools for Model Monitoring
Understanding ML Model Monitoring
Challenges in Monitoring ML Models
Exploring Model Drift Phenomenon
Operational Level Monitoring Insights
Introduction to Prometheus Monitoring System
WhyLabs Setup Process
Exploring WhyLogs Features: Drift Detection, Input/Output Monitoring, Bias Detection
WhyLogs Usage: Constraints and Drift Reports

Hands-on:

ML Model Monitoring
Monitoring using WhyLogs

Skills You will Learn

Model Monitoring and Debugging

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Kubeflow for Building ML Pipelines

Topics:

Kubeflow Introduction
Features
Kubeflow Fairing
Kubeflow Pipelines
Working with Kubeflow

Hands-on:

Building ML Pipeline with Kubeflow

Skills You will Learn

Building end-to-end ML pipelines with Kubeflow

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MLOps on Cloud

Topics:

Getting Started with Amazon Sagemaker
Amazon SageMaker Notebooks Overview
Configuration of Notebook Instance
Creating, Training, and Deploying ML Models with Sagemaker
Setting up Endpoints and Endpoint Configurations
Executing Inference from Deployed Models
Exploring SageMaker Studio & Domain Features
Introduction to SageMaker Projects
Understanding Repositories in SageMaker
Utilizing Pipelines and Graphs in SageMaker
Conducting Experiments with SageMaker
Managing Model Groups in SageMaker
Configuring Endpoints in SageMaker
Introduction to Azure Machine Learning Studio
Exploring Azure MLOps
Understanding Azure ML Components
Integration of Azure MLOps with DevOps
Setting up Fully Automated End-to-End CI/CD ML Pipelines

Hands-on:

Building an end-to-end MLOps pipeline using AWS SageMaker

Skills You will Learn

Using Amazon SageMaker for ML development and deployment
Management of ML Models and Experiments using Azure Machine Learning

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ML Model Post Deployment Challenges (Self-paced)

Topics:

Ensuring Model Integrity
Counteracting Adversarial Attacks
Guarding Against Data Poisoning
Preventing Distributed Denial of Service (DDOS)
Safeguarding Data Privacy
Strategies for Mitigating Model Attack Risks
Implementing A/B Testing Methodologies
Outlook on the Future of MLOps

Hands-on:

Implementing A/B Testing Methodologies

Skills You will Learn

Implementation of A/B testing methodologies for model evaluation
Strategies for Ensuring model Integrity and Security

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Course Details

About the MLOPS Course

This course offers a comprehensive introduction to MLOps, focusing on the effective deployment and management of machine learning models in production. It covers the entire ML lifecycle, including data preparation, model development, deployment, monitoring, and retraining. Students get hands-on experience with essential MLOps tools and frameworks such as LangChain, CrewAI, AutoGen, and retrieval-augmented generation (RAG), alongside core technologies like MLflow, Kubernetes, Docker, and cloud platforms, enabling them to build scalable, automated workflows that bridge data science and operations.

Key features:

Cloud-ready, practical, and hands-on training
Real-world projects for deploying and managing ML models at scale
Training on industry-standard MLOps tools and platforms
Focus on automating the ML lifecycle using best DevOps practices
Lifetime access to course materials and 24/7 technical support

Who should take up this Course?

This course is well-suited for programmers, developers, data analysts, statisticians, data scientists, and software engineers.

What are the prerequisites for this certification Course?

To excel in MLOps, you need Python skills, ML algorithm knowledge, experience with Docker, Kubernetes, and cloud platforms. A laptop with at least 8GB RAM, Intel i3+ processor, and stable internet is also required.

How will I execute the practicals in this course?

Practicals for this course will be implemented using various tools and detailed step-by-step installation guidesfor these tools are available on the LMS. In case you come across any doubt, the 24*7 support teams will promptly assist you.

What is this MLOps Certification Course?

A professional training course that teaches end-to-end MLOps such as development, deployment, monitoring, and operations, using key tools and techniques in line with industry standards.

Is this course interactive and project-based?

Yes, It includes live classes, hands-on labs, quizzes, and end-to-end real-world projects.

Which cloud platforms and tools are covered?

This training course covers AWS SageMaker, Azure ML, Google Cloud Platform, MLflow, Docker, Kubernetes, Jenkins, and more with AI tools.

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Testimonial Reviews

Chandra Bhushan KIT Consultant, Architect - Analytics in Karmanya Software Pvt Ltd, Hyderabad Area, India

Edureka has redefined the e-learning service with the help of technology. They have excellent facult ... Read More

Dheerendra YadavProject Lead at HCL Technologies, Ghaziabad, Uttar Pradesh, India

Earlier I had taken training in different technologies from other institutes and companies but no do ... Read More

Praveen KonkisaBI Architect & Hadoop Specialist at Teradata

I have taken Informatica, Hadoop, R-programming, Spark and Scala and several other training's from p ... Read More

Abhishek MishraExperience in Hadoop and Big Data Analytic, Java developer.

Awesome faculty. Awesome explanation on topics. I really appreciate Edureka Support team. They are r ... Read More

Suman RajaIT Analyst at Tata Consultancy Services Greater Philadelphia Area Information Technology and Services

Definitely there is no doubt in saying that all the instructors at Edureka are industry experienced ... Read More

Anil AlgolePrinciple Consultant at Infosys

Experience with Edureka is world class. I took 2 courses Informatica PowerCenter 9.x and Tableau Cer ... Read More

Video Reviews

Our learner Balasubramaniam shares his Edureka learning experience and how our training helped him stay updated with evolving technologies.

Sriram speaks about his learning experience with Edureka and how our Hadoop training helped him execute his Big Data project efficiently.

Vinayak shares his Edureka learning experience and how our Big Data training helped him achieve his dream career path.

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FAQs

What is MLOps?

MLOps, or Machine Learning Operations, is a set of practices designed to streamline the development, deployment, and maintenance of machine learning models, much like DevOps but tailored specifically for ML workflows.

Is this course suitable for working professionals?

ML Engineers focus on building models, while MLOps Engineers deploy and maintain them in production using DevOps practices.

Will I get placement assistance after completing this MLOps Training?

No, we do not provide placement assistance. The focus is on providing in-depth knowledge and hands-on skills to help you advance your career independently.

Who are the instructors for MLOps Course?

All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience.

What if I have more queries after completion of MLOps Course?

Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co.

What if I miss a class ?

You can easily catch up on missed lectures through recorded sessions in your LMS or by joining a live session in another batch.

What skills do I need to learn for MLOps?‎

You need strong programming skills, proficiency in infrastructure as code, an understanding of cloud services, and knowledge of machine learning concepts and frameworks to learn MLOps.

Why MLOps is Important?

Bridges the gap between data science and software engineering for faster and more reliable ML deployments.

Improves model performance and ensures higher reliability in production.

Automates and streamlines the ML lifecycle, enhancing efficiency and scalability.

What will be the mode and duration of this training?

We only offer live online training on weekends for a course duration of 5.5 weeks.

Does MLOps require coding?

Yes, MLOps generally requires coding knowledge because developers need it for MLOps to deploy models in production environments.

What is the cost of MLOps course in India?

The cost of the MLOps certification course in India is INR 21,999

Is MLOps better than DevOps?

MLOps focuses on automating machine learning tasks such as model training, while DevOps focuses on traditional software development tasks such as code deployment and builds.

What language is best for MLOps?

Python is the best language for MLOps

What is the significance of MLOps in the machine learning lifecycle?

The importance of MLOps in machine learning is to ensure faster deployment and easier troubleshooting.

What is the role of MLOps engineer?

They deploy, manage, and optimize machine learning models in production environments to ensure smooth integration and efficient operations.

What is the salary of MLOps analyst?

According to Glassdoor, the average salary for MLOps Engineer/Analyst is ₹11,64,260 per year in India.

Does MLOps have a future?

MLOps is a powerful and essential approach shaping the future of Machine Learning, and it's one of the most in-demand and rewarding skills to learn in 2024. With AI and ML being adopted across industries, MLOps roles are growing rapidly, offering strong career growth and competitive salaries from both a technical and business perspective.

What are the differences between DevOps and MLOps?

Both MLOps and DevOps are different. MLOps is based on Machine Learning While DevOps is based on development. In DevOps tools like Jenkins, GitLab CI, and Travis CI are used. In MLOps tools like MLflow, Weights & Biases are used.

What is the salary of MLOps in India?

MLOps Engineer salaries in India range from ₹6–10 LPA for entry-level, ₹10–20 LPA for 2–5 years of experience, and ₹20–35+ LPA for senior roles with strong cloud and CI/CD skills.

How long does it take to learn MLOps?

This course takes 5.5 Weeks to complete.

Who earns more, MLOps or data engineer?

In data science and machine learning, MLOps engineers typically earn more than data engineers due to their specialized skills in model deployment, automation, and cloud-based CI/CD pipelines.

Can beginners join this MLOps course?

Yes, If you have a basic background in Python and ML concepts, you can start this course

Do I need to know Docker and Kubernetes for MLOps?

Yes, basic knowledge of Docker and Kubernetes is important as they’re core tools for model deployment.

View course curriculum

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Structure your learning and get a certificate to prove it.

MLOps Certification Course

Why should you take Machine Learning Operations Certification Course?

Curriculum

MLOps Essentials

Version Control System

Packaging ML Models

Build MLApps using New Age Tools

CI/CD for ML Models

Machine Learning Model Management

Docker and Kubernetes for ML Deployment

Monitoring and Debugging of ML System

Kubeflow for Building ML Pipelines

MLOps on Cloud

ML Model Post Deployment Challenges (Self-paced)

Course Details

About the MLOPS Course

Who should take up this Course?

What are the prerequisites for this certification Course?

How will I execute the practicals in this course?

What is this MLOps Certification Course?

Is this course interactive and project-based?

Which cloud platforms and tools are covered?

Testimonial Reviews

Video Reviews

FAQs

What is MLOps?

Is this course suitable for working professionals?

Will I get placement assistance after completing this MLOps Training?

Who are the instructors for MLOps Course?

What if I have more queries after completion of MLOps Course?

What if I miss a class ?

What skills do I need to learn for MLOps?‎

Why MLOps is Important?

What will be the mode and duration of this training?

Does MLOps require coding?

What is the cost of MLOps course in India?

Is MLOps better than DevOps?

What language is best for MLOps?

What is the significance of MLOps in the machine learning lifecycle?

What is the role of MLOps engineer?

What is the salary of MLOps analyst?

Does MLOps have a future?

What are the differences between DevOps and MLOps?

What is the salary of MLOps in India?

How long does it take to learn MLOps?

Who earns more, MLOps or data engineer?

Can beginners join this MLOps course?

Do I need to know Docker and Kubernetes for MLOps?

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