What is MLOps and why is it important?

MLOps (Machine Learning Operations) applies DevOps practices to ML systems. It bridges the gap between ML development and production deployment. Important because 87% of ML projects never reach production - MLOps solves deployment, monitoring, and maintenance challenges.

How long does it take to learn MLOps?

With ML and DevOps background, 3-6 months for core MLOps skills. Without prior experience, 9-12 months including ML fundamentals, Docker, Kubernetes, and cloud platforms. Continuous learning is essential as the field evolves rapidly.

Do I need to know Machine Learning to do MLOps?

Yes, understanding ML fundamentals is essential. You need to understand model training, evaluation metrics, and common issues like overfitting. You don't need to be an ML researcher, but should understand the ML lifecycle and model behavior.

MLOps Engineer vs Data Engineer vs ML Engineer?

Data Engineers build data pipelines and infrastructure. ML Engineers develop and optimize ML models. MLOps Engineers focus on productionizing models - deployment, monitoring, CI/CD, and infrastructure. Significant overlap exists; roles vary by company.

Which MLOps tools should I learn first?

Start with Docker (containerization), MLflow (experiment tracking), and one cloud platform (AWS SageMaker or GCP Vertex AI). Add DVC for data versioning and Kubernetes for orchestration. Learn tools that solve real problems in your workflow.

Is MLOps a good career in 2026?

Excellent career choice. Demand for MLOps engineers exceeds supply significantly. As more companies adopt AI, they need MLOps to operationalize models. Salaries are high and growing. The field will remain critical as AI adoption accelerates.

Should I get MLOps certifications?

Cloud certifications (AWS ML Specialty, GCP Professional ML) add value. They demonstrate platform expertise. However, practical experience matters more. Build projects, contribute to open source, and understand real production challenges.

What's the difference between MLOps and DataOps?

DataOps focuses on data pipeline automation, data quality, and data governance. MLOps specifically addresses ML model lifecycle - training, deployment, monitoring, and retraining. MLOps often depends on DataOps for data infrastructure.

2026 Roadmap

MLOps Engineer Roadmap

Master ML pipelines, model deployment, CI/CD for ML, monitoring, Kubernetes, and production ML systems. Your complete guide to becoming an MLOps Engineer in 2026.

6-12 Months•Advanced•High Demand

What is an MLOps Engineer?

MLOps Engineers bridge the gap between Machine Learning development and production systems. They build and maintain the infrastructure, pipelines, and processes that enable ML models to run reliably in production at scale.

As an MLOps Engineer, you will automate ML pipelines, deploy models to production, set up monitoring and alerting, manage model versioning, and ensure ML systems are scalable, secure, and maintainable.

Key Responsibilities

Build and maintain ML pipelines
Deploy models to production environments
Set up experiment tracking and model registry
Implement CI/CD for ML workflows
Monitor model performance and data drift
Manage ML infrastructure on cloud platforms
Ensure reproducibility and governance

Learning Roadmap

Click on any topic to mark it as complete

Your Progress0/21 completed

ML & Programming Foundation

You understand ML basics!

Version Control & Collaboration

Containerization & Orchestration

You can containerize ML workloads!

ML Pipelines

Experiment Tracking

You can track experiments!

Model Deployment

CI/CD for ML

Cloud Platforms

You can deploy on any cloud!

Monitoring & Observability

Security & Governance

You are an MLOps Engineer!

Not Started

Completed

Milestone

MLOps Engineer Salaries 2026

United States (USD/Year)

Entry (0-2 yrs)

$100K - $130K

$115K

Mid (2-5 yrs)

$130K - $175K

$150K

Senior (5-8 yrs)

$170K - $220K

$195K

Staff/Lead (8+ yrs)

$200K - $280K+

$240K

India (INR/Year)

Entry (0-2 yrs)

₹12L - ₹20L

₹16L

Mid (2-5 yrs)

₹20L - ₹35L

₹27L

Senior (5-8 yrs)

₹35L - ₹55L

₹45L

Lead (8+ yrs)

₹50L - ₹80L+

₹65L

MLOps is one of the highest-paying specializations in tech. Companies struggle to find qualified MLOps engineers. Cloud platform expertise (AWS, GCP) and hands-on production experience command premium salaries. Remote positions at US companies offer excellent compensation.

Project Ideas

Build these to strengthen your portfolio

ML Pipeline with DVC

Beginner

Version controlled ML pipeline

DVCGitPythonScikit-learn

Model Serving API

Beginner

Deploy model with FastAPI

FastAPIDockerModel SerializationREST API

Experiment Tracking System

Intermediate

MLflow-based tracking

MLflowModel RegistryHyperparameter TuningMetrics

Kubernetes ML Platform

Intermediate

Deploy ML on Kubernetes

KubernetesHelmSeldon CoreMonitoring

End-to-End ML Platform

Advanced

Full MLOps platform

KubeflowFeature StoreCI/CDMonitoring

Real-time ML System

Advanced

Streaming ML predictions

KafkaFeature StoreLow LatencyAuto-scaling

Frequently Asked Questions

Related Roadmaps

Machine Learning

ML fundamentals

View Roadmap

DevOps Engineer

DevOps practices

View Roadmap

Data Engineer

Data infrastructure

View Roadmap

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