MLOps, or Machine Learning Operations, is the engineering discipline that connects data science with real-world production systems. This MLOps course 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. 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 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.
Enroll today. Batches are kept small - a maximum of 10 participants - so every session stays practical and focused.
Here are the key concepts you will learn during the training program.
This MLOps online course gives you hands-on experience with the specific tools that appear in job descriptions for MLOps engineers in 2026:
• Version Control & Experiment Tracking: Git, DVC, MLflow
• Containerization & Orchestration: Docker, Kubernetes, Kubeflow
• CI/CD Automation: Jenkins, GitLab CI
• Cloud Platforms: Azure Machine Learning, Amazon SageMaker, Google Cloud Vertex AI
• Monitoring & Observability: Grafana, Prometheus
• ML Frameworks: TensorFlow, Keras, Scikit-learn, PyTorch
• Programming & Scripting: Python, Linux/Bash
• Emerging Stack: Kubeflow, Hugging Face for production LLM pipelines
• Feature Stores: Feast, Tecton
• LLMOps Stack: LangChain, ONNX, Vector Databases
• Governance & Explainability: SHAP, Model Cards, Policy-as-Code
The demand for MLOps professionals has significantly outpaced the supply of qualified talent.
As of 2026, MLOps engineers in the United States earn between $110,000 and $180,000 per year, with senior roles at major tech firms often exceeding that range (Source: Glassdoor).
The global MLOps market is projected to reach $75.42 billion by 2033, growing at a CAGR of 43.2% from $2.08 billion in 2024 (Source: Market.us).
In India, MLOps engineers with two or more years of production experience can expect packages between ₹10 LPA and ₹28 LPA, with roles at product companies and AI-first startups trending toward the higher end. (Source: Brollyai)
The following are the high-paying roles you can choose after completing the training.
• MLOps Engineer
• Machine Learning Engineer
• AI/ML Platform Engineer
• ML Infrastructure Engineer
• AI/ML DevOps Engineer
• ML Reliability Engineer
• LLMOps Engineer (emerging and high-demand in 2026)
Amazon, Google, Microsoft, IBM, Flipkart, Swiggy, PhonePe, Zomato, Razorpay, and AI-focused startups across the US, UK, and India are among the organizations consistently recruiting for MLOps roles. They need engineers who can close the gap between experimentation and production at scale.
Learn directly from industry veterans with hands-on experience at top organizations like Microsoft, IBM, and Google. Our trainers do not just teach concepts- they bring real production challenges and solutions into every session.
Our graduates are employed at leading multinational companies across the US, UK, and India. When you join igmGuru, you join a network of professionals who have already made the transition successfully.
The MLOps landscape evolves fast. Our course outline is continuously updated to reflect the latest tools, frameworks, and industry practices- so what you learn today is what employers are looking for right now.
From resume building and LinkedIn optimization to mock interviews and placement assistance, igmGuru supports you well beyond the last session. The goal is not just certification- it is your next job offer.
All session recordings, course materials, and lab resources remain accessible to you indefinitely. Revisit any module, refresh your knowledge before an interview, or explore a tool again whenever you need to.
On successful completion of training, you receive an MLOps Certification by igmGuru. This is a verifiable credential you can add to your LinkedIn profile, resume, and professional portfolio. It signals to employers that you have not just studied MLOps in theory- you have built real systems and seen them through to deployment.
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.
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.
In certain aspects, MLOps is certainly better than DevOps. For instance, MLOps is faster. Since it comes with a machine learning model, highly accurate predictions are produced in a short span of time.
To know the cost of MLOps course in India, go to https://www.igmguru.com/machine-learning-ai/mlops-course-certification/#trainingOptions. You can also get in touch with us to know more about any running offers to get the best price.
Yes, MLOps require coding. If you wish to become an MLOps engineer, then you must have coding knowledge and a good working experience in the field.
To become a MLOps engineer, develop a strong foundation in machine learning, software engineering, and DevOps principles. Learn programming languages, version control systems, and cloud platforms. Familiarize yourself with MLOps tools like Docker, Kubernetes, and Jenkins. Gain practical experience by working on ML projects and stay updated with industry trends. Obtain relevant certifications and actively participate in MLOps communities. Continuously refine your skills, adapt to new technologies, and cultivate a problem-solving mindset. These steps will help you pave your way to becoming a proficient MLOps engineer.
Yes, MLOps engineers are in great demand as organizations become more dependent on machine learning models for making choices. They provide continuous model installation, observing, and scaling, closing the gap between data science and operations. As AI use grows in areas such as healthcare, finance, and technology, qualified MLOps workers play an increasingly important role to guarantee efficiency and maximum ROI.
Yes, igmGuru offers several other online certification courses. These include specialized online certification courses, tailored to different levels. igmGuru, greatly emphasizes upskilling and boosting career opportunities across any industry sectors, with each online certification course designed to help learners enhance their expertise.
Yes, igmGuru offers several other online courses under Machine Learning or Artificial Intelligence . These include specialized online courses, tailored to different skill levels. igmGuru greatly emphasizes upskilling and boosting career opportunities across IT industry sectors, with each online course designed to help learners enhance their expertise in Machine Learning or Artificial Intelligence . Below are few listed Courses.
MLOps Course
