MLOps (Machine Learning Operations) applies DevOps principles — automation, versioning, testing, CI/CD, and monitoring — to machine learning systems. It's needed because ML models don't behave like normal software: they degrade as real-world data changes, predictions drift over time, and a model that looked great in a notebook can fail silently in production. MLOps makes training, deployment, and retraining repeatable and monitored, so teams can ship models continuously, catch drift and data issues early, and avoid firefighting outages caused by broken pipelines or stale models.

DevOps handles the software delivery lifecycle: CI/CD, infrastructure automation, and keeping applications reliable in production. MLOps covers all of that plus the ML-specific layer — versioning datasets and models, tracking experiments, orchestrating training pipelines, managing GPUs, and monitoring for data drift and model decay rather than just uptime and latency. In simple terms, DevOps ships and operates code, while MLOps ships and operates code + data + models together. That extra ML lifecycle is the core difference between the two.

An MLOps engineer builds and runs the infrastructure that gets machine learning models into production and keeps them healthy. Typical work includes building training and retraining pipelines, containerising models and serving them on Kubernetes, setting up CI/CD for ML workflows, managing model registries and versioning, automating data validation, and monitoring deployed models for drift, latency, and accuracy drops. The end goal is simple: data scientists should be able to ship models quickly and safely, without manual deployments breaking and without anyone getting paged at 3 a.m.

The core stack is Python, Linux, Docker, Kubernetes, CI/CD pipelines, and at least one major cloud — AWS is the most demanded in India. On the MLOps-specific side, employers look for experiment tracking (MLflow), workflow orchestration (Airflow, Kubeflow), data versioning (DVC), model serving (TensorFlow Serving, BentoML, vLLM), infrastructure-as-code (Terraform), and model monitoring. Just as important is production judgment: debugging failing pipelines, handling GPU workloads, and keeping deployments reliable — skills that transfer directly from a DevOps background.

There are two common entry paths: from DevOps or software engineering into ML infrastructure, or from data science into deployment. The sequence is similar either way — build Python and Linux fundamentals, learn ML basics well enough to train and evaluate a model, master Docker and Kubernetes, then go deep on ML-specific tooling like MLflow, Airflow or Kubeflow, model serving, and monitoring. Build one end-to-end project that goes from data ingestion to a deployed, monitored model on a cloud like AWS — that single project carries more weight in interviews than certificates.

A practical beginner sequence: (1) Python and Linux fundamentals; (2) ML basics with scikit-learn so you understand what a model actually is; (3) Docker, then Kubernetes; (4) CI/CD with tools like GitHub Actions; (5) one cloud platform, ideally AWS; (6) MLOps-specific tooling — MLflow for tracking, Airflow or Kubeflow for orchestration, DVC for data versioning, a serving framework, and monitoring; (7) a capstone project where you train, deploy, and monitor a model end to end. With consistent effort, expect roughly four to six months if you already code, longer if you're starting from scratch.

Start from what you already have — Kubernetes, CI/CD, cloud, and observability are exactly what MLOps teams need. Next, fill the ML gap: learn how models are trained, evaluated, and served, and how data pipelines feed them. Then pick up the ML-specific layer: experiment tracking, model registries, orchestration tools like Airflow or Kubeflow, GPU-based inference, and drift monitoring. At your current job, volunteer for anything ML-adjacent — containerising a model, automating a training pipeline — and document one complete project on GitHub. With a few months of focused upskilling, most DevOps engineers can credibly interview for MLOps roles.

Since you already own the infrastructure side, the DevOps to MLOps roadmap compresses into the ML layers. Phase 1: ML fundamentals — training, evaluation, and data pipelines in Python. Phase 2: model serving — package a model in Docker and expose it as an inference API. Phase 3: Kubernetes for ML — GPU scheduling, autoscaling inference, and serving frameworks like KServe or vLLM. Phase 4: pipeline tooling — MLflow, Airflow or Kubeflow, DVC. Phase 5: monitoring and automated retraining, then a capstone project that ties everything together. DevOps engineers typically cover this in three to four months.

Salaries vary widely with experience and company type. Entry- and early-career MLOps engineers typically earn around ₹6–12 LPA, mid-level engineers with strong Kubernetes and pipeline experience commonly make ₹15–30 LPA, and senior or lead MLOps engineers at product companies and global capability centres often cross ₹35–50 LPA. Compensation rises fastest for people who can demonstrate real production ownership — running inference on Kubernetes at scale, automating retraining, and handling GenAI/LLM deployments, which are currently the hottest sub-skills in the Indian market.

Fresher-specific MLOps roles are relatively rare — most companies hire freshers into DevOps, SRE, or software engineering roles and let them specialise into MLOps within a year or two. Where entry-level MLOps or ML infrastructure roles do exist, typically at startups or through cloud and ML graduate programs, packages generally start around ₹4–8 LPA depending on city, company stage, and skills. Freshers who can already show Docker, Kubernetes, CI/CD, and one deployed ML project usually land at the higher end of that range.

Yes — demand is strong and growing faster than the supply of qualified people. Every company running serious ML or GenAI workloads, from startups to large product companies and global capability centres, needs engineers who can deploy and operate models reliably, and production ML infrastructure skills are far rarer than plain DevOps or data science skills. Most openings are concentrated in Bengaluru, Hyderabad, Pune, and Gurugram, along with remote roles for international teams. Look for titles like MLOps Engineer, ML Platform Engineer, AI Infrastructure Engineer, and deployment-focused Machine Learning Engineer.

No — a course can structure your learning, but it isn't mandatory. Many engineers switch through self-study: official documentation, open-source tools like MLflow, Kubeflow, and Airflow, and hands-on projects that end up in a GitHub portfolio, which is what interviewers actually assess. If you do prefer structured learning, judge any MLOps engineer course or bootcamp on whether it includes real deployment projects and Kubernetes practice, not just recorded videos. Another low-cost option is a focused 1:1 session with an experienced MLOps engineer to map your exact skill gaps before spending money on a full program.

An MLOps platform is a tool or managed suite that handles the ML lifecycle in one place — experiment tracking, dataset and model versioning, training pipelines, deployment, and monitoring. Managed examples include AWS SageMaker and Google Vertex AI, Kubernetes-native options include Kubeflow, and many teams assemble their own stack from open-source tools like MLflow, Airflow, and DVC. Platforms exist so teams don't hand-roll infrastructure for every model, and so moving a model from training to production stays standardised, reproducible, and auditable. The right pick depends on how much control you want versus how much you want managed for you.