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MLOps Certification Training Course
MLOps Certification Training Course
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Live Online Classes starting on 21st Jun 2025
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Instructor-led Introduction to MLOPS live online Training Schedule
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17,999
Starts at 6,000 / monthWith No Cost EMI Know more
Why enroll for MLOps Certification Training Course?
MLOps Course Benefits
The demand for MLOps engineers is rising across geographies due to the widespread adoption of AI and ML across industries and a recent report projected that the market will grow at a CAGR of 43.2% for the ensuing decade. You will acquire the knowledge to efficiently deploy, monitor, and manage machine learning models, along with practical experience in real-world scenarios, to be ready to navigate the intricacies of MLOps and progress in your professional career within the continuously changing technological environment.
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Annual Salary
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Annual Salary
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Why MLOps Certification Training Course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your MLOps Certification Training Course
MLOps Course Skills
Tools Covered
MLOps Course Curriculum
Curriculum Designed by Experts
MLOps Essentials
15 Topics
Topics:
- Introduction to MLOps
- MLOps vs. DevOps
- SDLC Basics
- Waterfall vs AGILE vs DevOps vs MLOps
- MLOps Phases
- Versioning
- Testing
- Automation
- Reproducibility
- Deployment
- Monitoring
- MLOPs Architecture
- ML Pipeline
- MLOps Tools
- MLOPs Case Study
![skill]( "Hands On Experience")
- MLOps Case Study
![skill]( "skill you will learn")
- Understanding MLOps Concepts
- Proficiency in SDLC Methodologies
Version Control System
7 Topics
Topics:
- Git Essentials
- Configuring Git
- Branching
- Git Workflow
- Repo
- Git Commands
- GitHub Action
![skill]( "Hands On Experience")
- Git Common Commands
- Branching and Merging
![skill]( "skill you will learn")
- Tracking and managing changes to code
- Source Code Management
- Tracking and Saving Changes in Files
Packaging ML Models
13 Topics
Topics:
- Model Packaging
- Experimentation
- Model Fitting
- Challenges in Working inside the Jupyter Notebook
- Create Config Module
- Data Handling Module
- Data Preprocessing
- Pipelines
- Training and Prediction
- Requirements.txt file
- Testing Virtual Environments
- Pytest
- Model Packaging and Testing
![skill]( "Hands On Experience")
- ML Model Packaging and Testing
![skill]( "skill you will learn")
- Model Packaging Techniques
- Data Preprocessing and Handling
- Testing and Validation of ML models
Build MLApps using New Age Tools
8 Topics
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
![skill]( "Hands On Experience")
- Developing ML models using Streamlit
- Building ML models using Flask
- Deploying ML models using FAST API
![skill]( "skill you will learn")
- ML Application Development
- ML Pipelines with FAST API
CI/CD for ML Models
13 Topics
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
![skill]( "Hands On Experience")
- Building CI/CD Pipelines
![skill]( "skill you will learn")
- Implementation of CI/CD Pipelines for ML
Machine Learning Model Management
9 Topics
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
![skill]( "Hands On Experience")
- Working with MLFlow
![skill]( "skill you will learn")
- Data Management
- Code Versioning
- Experiment Tracking
Docker and Kubernetes for ML Deployment
20 Topics
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
![skill]( "Hands On Experience")
- Docker CLI Commands
- Writing a Dockerfile Deployment
- DaemonSets
- Deploying Services
![skill]( "skill you will learn")
- Continuous Deployment
- Writing a Dockerfile to Create an Image
- Installing Docker Compose
- Configuring Local Registry
- Container Orchestration
- Application Deployment
Monitoring and Debugging of ML System
10 Topics
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
![skill]( "Hands On Experience")
- ML Model Monitoring
- Monitoring using WhyLogs
![skill]( "skill you will learn")
- Model Monitoring and Debugging
Kubeflow for Building ML Pipelines
5 Topics
Topics:
- Kubeflow Introduction
- Features
- Kubeflow Fairing
- Kubeflow Pipelines
- Working with Kubeflow
![skill]( "Hands On Experience")
- Building ML Pipeline with Kubeflow
![skill]( "skill you will learn")
- Building end-to-end ML pipelines with Kubeflow
MLOps on Cloud
18 Topics
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
![skill]( "Hands On Experience")
- Building an end-to-end MLOps pipeline using AWS SageMaker
![skill]( "skill you will learn")
- Using Amazon SageMaker for ML development and deployment
- Management of ML Models and Experiments using Azure Machine Learning
ML Model Post Deployment Challenges (Self-paced)
8 Topics
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
![skill]( "Hands On Experience")
- Implementing A/B Testing Methodologies
![skill]( "skill you will learn")
- Implementation of A/B testing methodologies for model evaluation
- Strategies for Ensuring model Integrity and Security
Course Details
About your MLOps Course
This course provides a comprehensive introduction to Machine Learning Operations (MLOps), focusing on the reliable and efficient deployment and management of ML models in production. It covers key stages like model development, deployment, monitoring, and optimization. Students will gain practical experience with leading MLOps tools and frameworks, equipping them to build scalable and production-ready ML workflows.
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.
How do I enroll for this training?
There are simple steps to enroll in this MLOps certification course:
- Check the Course Details
- Create an Account
- Click Enroll now
- Select your Batch and Timing
- Complete your Payment
- Get a confirmation mail
- Start learning
- Missed the current batch? Connect with our experts to join the next one.
Can I access the course materials after completing the training?
Edureka provides lifetime access to the course content. You can come back any time if you want to brush up your knowledge.
Projects
Automated Model Deployment and Scaling
Design an MLOps pipeline with the purpose of automating the deployment and scaling of machine learning models to accommodate varying workloads is crucial. The primary objective o....Dynamic Model Monitoring
Develop a framework for MLOps to ensure ongoing monitoring and updating of machine learning models using real-time data streams. The aim is to identify any deterioration, deviati....Certification
To unlock the Edurekaโs MLOps Certification Training Course, you must ensure the following:
- Completely participate in this MLOps Certification Training Course
- Evaluation and completion of the quizzes and projects listed
MLOps certification validates your ability to efficiently manage and deploy machine learning models, making you a valuable asset to operational and development teams.
After obtaining certification in MLOps, you may be qualified for a range of job roles. Some of the more specific job roles you may be qualified for include:
- MLOps Engineer
- MLOps Lead
- Devops & ML Engineer
- Associate Architect - MLOps
- MLOps Consultant
This MLops Concepts course covers model deployment, monitoring, version control, data pipelines, and hands-on experience with key MLOps tools like MLflow, Kubeflow, and TensorBoard.
John Doe
Title
with Grade X
XYZ123431st Jul 2024
The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate
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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.
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. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.
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?โ
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?
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 โน11,64,260 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.
Have more questions?
Course counsellors are available 24x7
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