Enroll in igmGuru’s online MLOps course and certification program and master the key concepts of managing and deploying machine learning models in real-world environments.
✅Level: Intermediate to Advanced ✅50-Hours Instructor-Led Training ✅Docker, K8s, MLflow, Jenkins, Airflow, Git, AWS SageMaker, Azure ML, Vertex AI ✅Real-World MLOps Labs, Live Projects & Case Studies ✅End-to-End ML Pipeline Development & Deployment ✅Resume Assistance, Mock Interviews & Certification Guidance ✅Updated Course Curriculum
MLOps, or Machine Learning Operations, is the engineering discipline that connects data science with real-world production systems. This MLOps course by igmGuru 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 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 online 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
Below are the topics you will cover in this MLOPs training program.
The following are the essential skills you will learn during this MLOps training program.
This MLOps online course gives you hands-on experience with the specific MLOps tools that appear in job descriptions for MLOps engineers in 2026:
This MLOps Course is built around real-time practical projects that take you beyond theory and isolated model experiments. You will work on realistic machine learning workflows where the focus is on automation, deployment, monitoring, scalability, and keeping models reliable in production.
The following are the high-paying roles you can choose after completing the MLOps training.
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+ |
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.
On successful completion of the training, you'll receive an MLOps certification from igmGuru, and it stands for something real. This isn't a certification only for showing up and watching videos. It's proof that you've actually built the kind of systems companies are struggling to hire for: models that don't just work in a notebook, but survive contact with the real world.
To earn it, you'll get your hands across the full pipeline- deploying models, automating CI/CD, containerizing with Docker and Kubernetes, setting up monitoring that catches problems before they blow up, and pushing deployments live on AWS or Azure. Every skill on that certificate is something you've actually done, not just watched someone else do on screen.
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.
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.
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.
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.
Note: 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.
Unlike many MLOps Courses, igmGuru combines live instructor-led training, hands-on labs, real-world projects, and expert mentorship to help you master end-to-end MLOps and confidently apply your skills in production environments.
Data scientists, ML engineers, software developers, and DevOps professionals looking to work with production ML systems.
Yes, you'll receive a course completion certificate after finishing all modules and assignments.
You'll work with MLflow, Docker, Kubernetes, Airflow, and cloud platforms like AWS, Azure, and GCP.
Both options are available- choose self-paced learning or live instructor-led sessions.
Yes, the course includes hands-on projects covering end-to-end ML pipeline deployment.
Yes, you'll get placement assistance along with interview preparation support.
MLOps Course
