What is mlops?

MLOps (Machine Learning Operations) is a set of practices that combines machine learning (ML) with DevOps (development and operations). It helps teams build, deploy, monitor, and maintain ML models quickly and reliably, turning data science code into production-ready software.

Let's break it down

  • Model building: Data scientists create and train a model using data.
  • Version control: Both code and data are stored in repositories so changes can be tracked.
  • Continuous integration (CI): New code is automatically tested to make sure it works.
  • Continuous delivery (CD): Tested models are packaged and sent to a staging or production environment.
  • Monitoring: Once live, the model’s performance and resource usage are watched for drift or failures.
  • Feedback loop: Real‑world results are fed back to improve the model over time.

Why does it matter?

MLOps makes it easier to move from a prototype to a reliable product. It reduces manual steps, cuts down errors, speeds up updates, and ensures models stay accurate as data changes. This reliability is crucial for businesses that depend on AI for decisions, customer experiences, or automation.

Where is it used?

  • Online retail (personalized recommendations)
  • Finance (fraud detection, credit scoring)
  • Healthcare (diagnostic assistance, patient risk prediction)
  • Manufacturing (predictive maintenance)
  • Any company that wants to embed AI into apps, services, or internal tools.

Good things about it

  • Faster delivery of new models to users.
  • Consistent, repeatable processes that lower risk of bugs.
  • Better collaboration between data scientists, engineers, and ops teams.
  • Automated monitoring catches performance drops early.
  • Scalable infrastructure can handle growing data and traffic.

Not-so-good things

  • Requires upfront investment in tooling, training, and culture change.
  • Can become complex; too many pipelines may be hard to manage.
  • Monitoring ML models is harder than monitoring regular software because of data drift and bias.
  • Not all organizations have the expertise to set up a full MLOps stack, leading to partial or ineffective implementations.