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Technical Vocabularyabbreviation · /ɛm.ɛl.ɑːps/

MLOps

In simple English

MLOps stands for Machine Learning Operations. It emerged as companies realized that building machine learning models is only part of the challenge—deploying them, monitoring them, and updating them in production requires a dedicated set of processes and tools. MLOps brings together data scientists, engineers, and DevOps specialists to streamline model training, testing, deployment, and monitoring. In practice, MLOps means using automated pipelines to retrain models, version control for datasets and model artifacts, continuous integration and continuous deployment (CI/CD) for ML, and robust monitoring systems to catch model drift or performance degradation. Many tech companies now have entire teams dedicated to MLOps infrastructure because managing thousands of models across different environments is complex and requires specialized expertise.

What is MLOps and why do tech teams need it?

At a glanceCEFR C1
Commonness4/5
Versatility4/5
FormalityCasualFormalNeutral
Spoken ↔ WrittenSpokenWrittenBoth
Directness ↔ DiplomaticDirectDiplomaticDirect
RegionUS and UK
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Say it like this

✓We need to invest in MLOps infrastructure
✓The MLOps pipeline handles model deployment automatically
✓Our MLOps team manages all production models

Real examples

in a standup

"The MLOps team improved model serving latency by 40% this sprint using containerization."

in a design review

"We should establish MLOps best practices before scaling to hundreds of models."

in an interview

"At my previous company, I led the MLOps initiative that reduced deployment time from weeks to days."

Don't say this

Handle with care. MLOps is not a verb and should not be used as an action. It refers to a practice or set of tools, not something you do to something else.

✕We need to MLOps the model before launch.
✓We need to implement MLOps practices before launching the model.— MLOps is a noun referring to practices and tools, not a verb you perform on something.
✕MLOps is just about deploying machine learning models.
✓MLOps encompasses the entire lifecycle of ML models, from development through monitoring in production.— MLOps includes much more than deployment—it covers training, versioning, monitoring, and retraining cycles.
✕We do MLOps using Jenkins.
✓We use Jenkins as part of our MLOps infrastructure.— Jenkins is a tool used in MLOps workflows, but MLOps itself is the broader practice.

Other forms

noun

MLOps

"MLOps requires collaboration between data scientists and DevOps engineers."

noun

MLOps team

"The MLOps team manages model versioning and deployment pipelines."

noun

MLOps platform

"We chose Kubeflow as our MLOps platform for orchestrating workflows."

Often used with

MLOps infrastructureThe underlying systems and tools that support ML model management
MLOps pipelineThe automated workflow for training, testing, and deploying models
MLOps platformSoftware tools designed to manage the ML lifecycle, like Kubeflow or Databricks
MLOps best practicesEstablished methods and standards for managing ML in production
MLOps teamThe group responsible for designing and maintaining ML operations

Similar words

ML OperationsThe full form of MLOps, used interchangeably but less common in speech
ML lifecycle managementBroader term that includes MLOps but also covers earlier development stages

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Practice

Try it

4 quick exercises

Complete the sentence

1. Our company invested heavily in _______ to reduce model deployment time.

Spot the mistake

2. Which sentence uses MLOps incorrectly?

Pick the most natural phrasing

3. You want to explain that your company manages many production models well. What do you say?

Rewrite naturally

4. Rewrite using MLOps terminology: 'We need to automatically update and monitor our machine learning models in production without manual work.'

Questions

Quick poll

Have you said "mlops" in a standup this week?

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