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Profession

ML Ops Engineer

AI in production with confidence.

A machine learning operations engineer builds and runs the systems that take machine learning models from a data scientist's notebook to a reliable product. Your days involve setting up pipelines for training and deployment, versioning data and models, monitoring performance in production, and fixing things when predictions drift or services slow down. You work closely with data scientists, software engineers, and cloud or platform teams, and much of your value comes from making their work repeatable and safe to ship.

Average salary

$9,000 – $22,000

Education

4 years

Employability

91%

🎥 See what it's really like

What Is MLOps?

IBM Technology gives a clear overview of what MLOps engineers do to take models from notebooks into production.

MLOps Explained

Microsoft Azure explains the MLOps workflow and the responsibilities that come with running machine learning in production.

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❓ Frequently asked questions

How much does a ML Ops Engineer professional earn?▾

Salary ranges from $9,000 – $22,000 per month, depending on specialization, region, and experience. Professionals in major cities tend to earn above the national average.

Education: 4 years

What education is required for ML Ops Engineer?▾

4 years. Beyond the degree, many professionals pursue specializations or postgraduate studies to stand out.

Areas of practice

MLflowKubeflowFeature Store
What are the main practice areas in ML Ops Engineer?▾

The main areas are: MLflow, Kubeflow, Feature Store. The employability rate in this field is 91%.

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