MLOps Certification Course Training
Become a production-ready MLOps Engineer through hands-on practical training, real-world projects, and industry-oriented workflows used in modern AI and machine learning environments.
12+
Years Experienced
Professional Coaches
8,000+
Learners
Trained
7000+
Career Transformations
9+
Years Industry Experience
In Training
70+
Hrs Hands-on
Training
4+
Production-Grade
Projects
Why Choose Our MLOps Certification Course?
- Build 4+ Real-Time Projects to Gain Practical Production-Level Experience
- Receive Industry-Oriented MLOps Engineer Certification from JingleAI Academy.
- Practical and Job-Oriented Curriculum Designed to Build Future-Ready AI & MLOps Skills
- Learn from Industry-Experienced Mentors with Real-World Production Exposure
- Dedicated Learner Support for Doubt Clarification, Guidance, and Career Assistance
- Access all class Recordings, Assignments, Labs and Notes Through Our LMS.
Our Learners Work at Leading IT, Cloud & AI Companies
Our learners are working in top IT, Cloud, and AI companies across roles such as MLOps Engineer, AI DevOps Engineer, Cloud AI Engineer, LLMOps Engineer, AI Infrastructure Engineer, and Platform Engineer.
MLOps Engineer Salary in India, Career Growth & Future Demand
The rise of AI is creating a growing demand for MLOps professionals.
| Experience Level | Estimated Salary Range |
|---|---|
| Beginner (0-3 Years) | ₹6 LPA – ₹12 LPA |
| Mid-Level (3-7 Years) | ₹12 LPA – ₹28 LPA |
| Senior (7+ Years) | ₹28 LPA – ₹60+ LPA |
Global demand for AI Infrastructure and MLOps Engineers is increasing rapidly.
Career Opportunities After Completing Our MLOps Master Certification Course
Our MLOps course training is designed to provide a comprehensive and broad skill set, preparing learners for all of these roles.
MLOps Engineer
AIOps Engineer
AI DevOps Engineer
Cloud AI Engineer
AI Infrastructure Engineer
ML Platform Engineer
ML Reliability Engineer
LLMOps | AgenticOps Engineer
Career Opportunities and Future Scope. Launch your career in the rapidly growing AI and Machine Learning industry.
Upcoming MLOps Course Batches & Training Schedule
MLOps Live Online and Classroom Training
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Training Duration: 70+ hours of practical, instructor-led MLOps training
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Training Mode: Live online and classroom-based interactive sessions
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Learning Approach: Hands-on assignments, labs, and real-world projects
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Program Length: Structured 3-month industry-oriented training program
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Batch Options: Flexible weekday and weekend learning schedules
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Access Included: Session recordings, LMS access, notes, and assignments
Choose Batch Later ->
We'll reach out to assist you with batch selection.
₹ 29,999 /-
|$ 445 USD
( MLOps Certification Included )
EMIs start at ₹ 5000 / month
( No cost EMI option available )
Secure Transaction
Self-Paced MLOps Certification Course
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Get instant access to complete MLOps course recordings and learn anytime, from anywhere at your own pace.
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No fixed class schedules — ideal for working professionals, students, freelancers, and shift-based employees.
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Access practical assignments, labs, notes, and real-world learning resources through our LMS platform.
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Get mentor guidance and doubt support whenever needed during your self-paced learning journey.
₹ 29,999 /- | $ 445 USD
( MLOps Certification Included )
EMIs start at ₹ 5000 / month
( No cost EMI option available )
Secure Transaction
Industry-Recognized MLOps Course Completion Certificate
What certificate will I receive after completing the MLOps course?
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Learners who successfully complete the training program will receive an official MLOps Course Completion Certificate from JingleAI Academy.
Is this certificate useful for jobs and interviews?
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The certificate demonstrates your commitment to learning modern MLOps and AI infrastructure skills. Combined with hands-on projects and practical knowledge gained during the program, it can strengthen your profile for interviews and career opportunities.
Is this a certification exam or a course completion certificate?
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This is a course completion certificate awarded to learners who successfully complete the MLOps training program, assignments, and practical learning activities.
ML Ops Course Curriculum and Learning Path
MLOps Tools Covered: This MLOps Certification Course provides hands-on training on industry-leading tools including MLflow, DVC, Apache Airflow, FastAPI, Docker, Kubernetes, Jenkins, KServe, Prometheus, Grafana, Kubeflow, and other modern MLOps technologies used to build, deploy, automate, monitor, and manage production-grade machine learning systems.
DevOps DevOps Foundation Course (60 Hours) - (Pre-requisite) (Self-paced) - FREE
Git
GitHub Version Control System
GitHub Version Control System
- Version Control Fundamentals
- Local & Remote Repositories
- Core Git commands for Code Management
- Branching Strategies
- Merging & Conflict Resolution
- GitHub Actions
- Team Collaboration with GitHub
Maven Built Automation Tool
- Maven Architecture & Lifecycle
- Project Object Model (POM) files
- Dependency Management
- Maven Repositories
- Build Automation
- Packaging & Artifact Generation
- Maven Best Practices
- Project - Gamutkart Application End-to-end Build and Deployment Automation
Jenkins CI/CD Automation Platform
- Continuous Integration (CI)
- Continuous Delivery & Deployment (CD)
- Declarative & Scripted Pipelines
- Distributed Builds & Agents
- Jenkins Plugins Ecosystem
- Integration with Git & GitHub & Maven
- Docker & Kubernetes Integratio
- Secure Credential Management
- Project - Gamutkart E-commerce Application End-to-end Build and Deployment Pipeline
Docker Containerization and Application Packaging Platform
- Docker Architecture
- Container Fundamentals and Commands
- Virtualization Vs containerization
- Creating Docker Images & Dockerfile's
- Docker Compose
- Image Registry Management
- Data Persistence & Volumes
- Project - Containerizing Gamutkart Application
Kubernetes Container Orchestration Platform
- Kubernetes Architecture
- Pods & Workloads
- Deployments & Services
- ConfigMaps & Secrets
- Auto Scaling & High Availability
- Rolling Updates & Rollbacks
- Gamutkart Project Deployment in K8S cluster - On-premise & Cloud
- Project - Gamutkart Project With GKE Services
Python Python Programming (30 Hours) - (Pre-rquisite) (Self-paced) - FREE
- Python Fundamentals
- Variables & Data Types
- Control Flow & Loops
- Functions & Modules
- Object-Oriented Programming (OOP)
- File Handling
- Python Standard Libraries
- Data Structures & Algorithms
- Data Analysis with NumPy & Pandas
- Data Visualization with Matplotlib
- Real-World Python Projects
MLOps MLOps Certification Course (70+ hours) - (Live, Instructor-led)
- MLOps Tools -> DVC - DagsHub - MLflow - Airflow - Feast - FastAPI - Docker for MLOps - Kubernetes for MLOps - Jenkins for MLOps - KServe - Prometheus - Grafana - Kubeflow - 4 End-to-End Capstone Projects.
- Complete MLOps Journey - From Data to Production
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- Data Versioning ->Experiment Tracking -> Pipeline Orchestration -> Feature Store -> Model Serving -> Containerization -> Kubernetes Deployment -> CI/CD Automation -> Monitoring & Observability -> Enterprise MLOps
DVC Data Version Control
- Data and Model Versioning
- Dataset Lifecycle Management
- Remote Artifact Storage
- Reproducible ML Pipelines
- Experiment Management
- Git + DVC Collaboration
- Model Artifact Tracking
- Production MLOps Best Practices
DagsHub MLOps Collaboration Platform
- Collaborative MLOps Workflows
- Data, Code, and Model Management
- DVC Integration
- Experiment Tracking
- Team Collaboration
- Artifact Sharing
- Centralized ML Project Management
- MLOps Governance
MLflow ML Lifecycle Management Platform
- Experiment Tracking
- Metrics, Parameters, and Artifact Logging
- Model Registry
- Model Version Management
- Model Lifecycle Management
- Model Deployment Concepts
- MLflow Tracking Server
- Production MLOps Workflows
Apache Airflow Data & ML Pipeline Orchestration Platform
- Workflow Orchestration
- DAG Development
- Task Scheduling and Automation
- Data and ML Pipelines
- Automated Model Training
- Workflow Monitoring
- Airflow + MLflow Integration
- Production Pipeline Management
FastAPI ML Model Serving Framework
- Building REST APIs
- Model Serving Fundamentals
- Real-Time Inference APIs
- Request and Response Validation
- API Documentation
- Model Integration
- Secure API Development
- Production Model Serving
Docker for MLOps Application Containerization Platform
- Docker Images and Containers for MLOps
- Containerizing ML Applications
- Reproducible Runtime Environments
- Docker Compose
- ML API Packaging
- Registry Management
- Kubernetes-Ready ML Deployments
Kubernetes for MLOps Cloud-Native Container Orchestration Platform
- Kubernetes for MLOps
- Deploying ML Applications
- Scaling Model Serving Workloads
- Persistent Storage Management
- ConfigMaps and Secrets
- High Availability ML Deployments
- GPU Workload Concepts
- Production MLOps Infrastructure
Jenkins for MLOps CI/CD, ML Pipeline Automation Platform
- CI/CD for Machine Learning
- Automated Model Training Pipelines
- Continuous Integration Workflows for MLOps
- ML Continuous Deployment Pipelines
- Docker Build Automation
- Kubernetes ML Deployments
- Model Release Automation
- MLOps Pipeline Automation
KServe Kubernetes-Native Model Serving Platform
- Kubernetes-Native Model Serving
- Inference Services
- Real-Time Predictions
- Autoscaling Model Deployments
- Canary Rollouts
- Multi-Model Serving
- MLflow Integration
- Production Model Serving
Prometheus Metrics Collection and Monitoring Platform
- Metrics Collection
- Infrastructure Monitoring
- Application Monitoring
- Time-Series Data Management
- Custom Metrics
- PromQL Queries
- Alerting and Notifications
- MLOps Observability
Grafana Visualization and Monitoring Dashboard Platform
- Monitoring Dashboards
- Data Visualization
- Prometheus Integration
- Infrastructure Observability
- Model Serving Dashboards
- Alert Management
- Operational Insights
- End-to-End Monitoring
Kubeflow Machine Learning Workflow Platform
- Kubeflow Architecture
- Kubeflow Pipelines
- End-to-End ML Workflow Automation
- Distributed Training
- Hyperparameter Tuning
- Experiment Management
- Model Deployment Workflows
- Enterprise MLOps Platforms
Projects End-to-End Capstone Projects
- Project-1, JingleKart AI E-Commerce Platform ( Similar To Flipkart )
- Project-2, EstateIQ Real Estate Prediction System ( Similar to Zillow )
- Project-3, CreditWise Fraud Detection ( Similar to PayPal / American Express Risk Systems )
- Project-4, Based on New Trends
Production-Grade MLOps Projects Included in the Course
E-Commerce Recommendation System MLOps Project
(Similar To Flipkart)
Build and deploy an AI-powered recommendation system while implementing model training, deployment, CI/CD automation, Kubernetes deployment, monitoring, and end-to-end MLOps workflows.
Build and deploy an AI-powered recommendation system while implementing model training, deployment, CI/CD automation, Kubernetes deployment, monitoring, and end-to-end MLOps workflows.
House Price Prediction MLOps Project
(Similar to Zillow)
Develop a production-ready prediction platform while implementing automated ML pipelines, model deployment, CI/CD automation, Kubernetes deployment, monitoring, and end-to-end MLOps workflows.
Develop a production-ready prediction platform while implementing automated ML pipelines, model deployment, CI/CD automation, Kubernetes deployment, monitoring, and end-to-end MLOps workflows.
Fraud Detection MLOps Project
(Similar to PayPal / American Express Risk Mgmt. System)
Create and deploy a fraud detection platform while implementing model training, automated deployment, CI/CD automation, Kubernetes deployment, monitoring, and end-to-end MLOps workflows.
Create and deploy a fraud detection platform while implementing model training, automated deployment, CI/CD automation, Kubernetes deployment, monitoring, and end-to-end MLOps workflows.
70+ Hours of Hands-On MLOps Training with Real-World Projects
While many programs focus on quick overviews, JingleAI Academy's MLOps curriculum ensures deep understanding, practical mastery, and job-ready MLOps skills.
Success Stories from DevOps & Cloud Professionals Transitioning to MLOps
Arvind Basappa
I was working randomly on many things like DevOps, cloud, Infrastructure, Deployments ..etc. It was more of support role. I was not sure about my exact role. One of my friends reffered JingleAI academy's MLOps course and joined. The course explained MLOps concepts step by step and now I have a proper role MLOps Engineer.
Who Can Take The MLOps Certification Course Training?
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DevOps Engineers
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Cloud Engineers
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Platform Engineers
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Infrastructure Engineers
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Aspiring DevOps Engineers
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Infrastructure Engineers
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Software Developers & Testers
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Data Analysts & BI Professionals
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Machine Learning & AI Enthusiasts
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Freshers & Final Year Students
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Career Switchers & Working Professionals
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New College Graduates
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Individuals trying to get into Software field
MLOps Corporate Training Programs for Your Teams
Practical & Project-ready MLOps training customized for enterprise teams
ML Ops Course FAQs
What is MLOps?
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MLOps (Machine Learning Operations) is the practice of deploying, automating, monitoring, and managing machine learning models in production using DevOps principles, practices, cloud platforms, and automation tools.
Is MLOps a good career in 2026?
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Yes. MLOps is one of the fastest-growing careers in AI. As organizations adopt more AI and machine learning solutions, the demand for skilled MLOps Engineers who can deploy, automate, monitor, and manage machine learning systems continues to grow. It offers strong career opportunities and long-term growth potential.
What is the salary of an MLOps Engineer in India?
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MLOps Engineers in India can earn attractive salaries based on experience and skills. Beginners (0–3 years) typically earn ₹6–12 LPA, mid-level professionals (3–7 years) earn ₹12–28 LPA, and senior MLOps Engineers (7+ years) can earn ₹28–60+ LPA. With growing AI adoption, MLOps remains one of the most in-demand and high-paying technology careers.
Can a DevOps Engineer transition to MLOps?
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Absolutely. DevOps professionals already possess many foundational skills such as Linux, Docker, Kubernetes, CI/CD, Cloud, and Automation, making MLOps a natural career progression.
MLOps will take DevOps Engineers to AI/ML World.
Can a Cloud & Platform Engineer transition to MLOps?
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Yes. Cloud and Platform Engineers already have strong foundations in cloud infrastructure, Docker, Kubernetes, automation, and CI/CD. By learning machine learning fundamentals and MLOps practices, they can successfully transition into MLOps Engineer roles.
Is DevOps and Python mandatory for MLOps?
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Yes, having a basic understanding of DevOps and Python is highly beneficial for learning MLOps.
That's why learners who enroll in our MLOps Master Certification Course receive 60+ hours of DevOps training and 30+ hours of Python training in self-paced mode at no additional cost.
Do I need prior DevOps, Machine Learning and Python experience to learn MLOps?
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No. This course covers the essential AI, Machine Learning, DevOps, Python fundamentals required to help learners understand and implement production-grade MLOps.
What MLOps tools are required to become an MLOps Engineer?
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To become an MLOps Engineer, you typically need to learn tools such as DVC, MLflow, Apache Airflow, FastAPI, Docker, Kubernetes, Jenkins, KServe, Prometheus, Grafana, and Kubeflow. The exact tools required may vary depending on the project.
Do I need to pay separate fees for learning DevOps and Python?
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No. Learners who enroll in our MLOps Master Certification Course receive complimentary access to our 60+ hours of DevOps training and 30+ hours of Python training in self-paced mode. These foundational modules are designed to help learners strengthen their DevOps and Python skills before diving into advanced MLOps concepts.
This means you get access to DevOps, Python, Machine Learning Fundamentals, and MLOps—all as part of a single learning path, with no additional fees.
Who should enroll in this MLOps Master Certification Course?
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This course is ideal for anyone looking to build a career in MLOps. Whether you're a fresher, DevOps Engineer, Cloud Engineer, Software Developer, Data Engineer, or a professional transitioning into AI and Machine Learning, this program covers all the required prerequisites to help you succeed.
Are real-world MLOps projects included in the course?
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Yes. We don't believe in just theory. The course includes 4+ hands-on, real-world MLOps projects that help you gain practical experience in deploying, managing, and monitoring machine learning systems.
Will I receive an MLOps Certification after completing the course?
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Yes. Learners who successfully complete the course requirements will receive a JingleAI Academy MLOps Certification.
How is MLOps different from DevOps?
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Using DevOps, we build, deploy, and manage software applications. MLOps follows many of the same DevOps principles but focuses on deploying and managing Machine Learning models.
MLOps manages the entire machine learning lifecycle, including model training, versioning, deployment, monitoring, and governance.
What makes JingleAI Academy's MLOps Master Certification Course different?
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This comprehensive Master Program combines Machine Learning, Python, DevOps, Cloud, Automation, MLOps Tools, and Real-World Projects to prepare learners for multiple AI and MLOps-related career opportunities.
