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.
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.
What certificate will I receive after completing the MLOps course?
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?
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?
This is a course completion certificate awarded to learners who successfully complete the MLOps training program, assignments, and practical learning activities.
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.
AWS Cloud for MLOpsDVCDagsHubMLflowAirflowFastAPIDocker for MLOpsKubernetes for MLOpsKubeflowJenkins for MLOpsKServePrometheusGrafana4 Capstone Projects
End-to-End Enterprise MLOps Workflow
📦
Data Versioning
🧪
Experiment Tracking
⚙️
Model Training
🗂️
Model Registry
🚀
Model Deployment
🐳
Model Serving
☸️
Monitoring & Observability
🔁
Model Retraining
📊
Production MLOps
📥
Data Ingestion
🗃️
Data Versioning
🧪
Experiment Tracking
🔄
Model Training
🧩
Model Registry
🤖
Model Deployment
🐳
Model Serving
☸️
Monitoring & Observability
⚡
Model Retraining
📈
Production MLOps
DevOps FREEDevOps Foundation Course (60 Hours) - (Pre-requisite) (Self-paced)
Git GitHubVersion 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
MavenBuilt 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
JenkinsCI/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
DockerContainerization 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
KubernetesContainer 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
Apache AirflowData & 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
FastAPIML 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 MLOpsApplication 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 MLOpsCloud-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 MLOpsCI/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
KServeKubernetes-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
PrometheusMetrics Collection and Monitoring Platform
Metrics Collection
Infrastructure Monitoring
Application Monitoring
Time-Series Data Management
Custom Metrics
PromQL Queries
Alerting and Notifications
MLOps Observability
GrafanaVisualization and Monitoring Dashboard Platform
Monitoring Dashboards
Data Visualization
Prometheus Integration
Infrastructure Observability
Model Serving Dashboards
Alert Management
Operational Insights
End-to-End Monitoring
KubeflowMachine 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
ProjectsEnd-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.
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.
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.
Trending Technologies Capstone Project
(Future Skills Innovation Project)
This project is regularly updated to align with current market demands, helping learners gain hands-on experience with modern AI, MLOps, and Automation technologies.
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
Ananya HegdeSenior DevOps Engineer
JingleAI Academy’s MLOps course helped me move from DevOps to MLOps with clear hands-on training. The real-time projects, MLflow, Docker, Kubernetes, and CI/CD practice made me confident for MLOps engineer roles.
K. Laxman Rao DevOps Engineer
I was working as a DevOps Engineer with more of support things. I heard about their DevOps to MLOps career transition program and joined. I learned Python, ML fundamentals and MLOps tools. This course helped me to change my job and get placement in a good company with better package.
Varun Gowda Cloud Engineer
I come from cloud and a bit of basic DevOps knowledge background. I learned machine learning fundamentals, Indepth DevOps and MLOps from very basics to advanced. Instructors are good and very supportive in entire learning journey.
Deepa Kulkarni Lead DevOps Engineer
Instructors are very knowledgeable and professional. What I like most is their hands-on practicals and project work. After working 8 years in the same company as DevOps Lead, I am able to land in MLOps & AI world now. Thank you.
Rahul S. ShettigarDevOps Engineer
MLOps course curriculum is very comprehensive. I think the way they teach, meterials, recorded sessions, Hands-on project work, Assignments benefits more worth than the fees they charge.
Meera NayakPlatform Engineer
My company started implementing ML & AI modules in my application. And I had to learn MLops to automate production pipelines. This MLOps course training helped me understand ML pipelines, Kubernetes deployment, model monitoring, and automation.
Arvind BasappaOperations Engineer
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.
Sahana MurthyFresher
I was new to machine learning and studied Python in my college. I don't wanted to land in coding havy job. One of my brother's friend suggested that MLOps is advanced to DevOps and MLOps Engineers work in AI/ML. I joined the course and now working as Intern MLOps engineer.
Who Can Take The MLOps Certification Course Training?
DevOps Engineers
Cloud Engineers
Platform Engineers
Infrastructure Engineers
Aspiring DevOps Engineers
Infrastructure Engineers
Software Developers & Testers
Data Analysts & BI Professionals
Machine Learning & AI Enthusiasts
Freshers & Final Year Students
Career Switchers & Working Professionals
New College Graduates
Individuals trying to get into Software field
MLOps Corporate Training Programs for Your Teams
Practical & Project-ready MLOps training customized for enterprise teams
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
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?
Yes. Learners who successfully complete the course requirements will receive a JingleAI Academy MLOps Certification.
How is MLOps different from DevOps?
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?
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.
