Machine learning operations (MLOps)

What is it?

MLOps (Machine Learning Operations) is a set of:

  • Practices that integrate the development, deployment, and management of machine learning (ML) models.
  • Tools designed to optimise and automate the model lifecycle.

Inspired by DevOps principles, MLOps aims to optimise the ML lifecycle:

  • Development.
  • Testing.
  • Deployment to production.
  • Continuous monitoring.
  • Updating and retraining.

The primary objective of MLOps is to ensure that ML models are:

  • Reliable, performing correctly in production.
  • Scalable, handling large volumes of data.
  • Easy to maintain, with efficient updates.

It also aims to:

  • Accelerate time to delivery.
  • Improve collaboration between:
    • Data scientists.
    • ML engineers.
    • Operations teams.

Why filter companies by their usage?

Segmenting by MLOps usage allows you to tailor commercial strategies:

  • Advanced companies: Help them refine workflows and scale ML operations.
  • Companies without MLOps: Guide them towards structured and efficient processes for deploying machine learning in production.

Companies that do use it

These companies have already integrated advanced ML practices and are likely interested in:

  • Improved automation to reduce manual work and errors.
  • Scalability for managing data and models at production scale.
  • Advanced monitoring to ensure ongoing model performance.

Your sales team could offer:

  • Optimisation of data and model pipelines.
  • Real-time monitoring tools to detect failures or model drift.
  • Integration of MLOps with existing analytics platforms or hybrid cloud environments.

Companies that do not use it

These companies may be:

  • Developing ML models without standardised processes.
  • Deploying models manually to production, increasing risk and operational overhead.

This can lead to:

  • Process inefficiencies.
  • Less reliable or outdated models.

Your sales team could offer:

  • Initial consulting to assess needs and define workflows.
  • Implementation of basic MLOps tooling to standardise and scale processes.
  • Training and support on deployment, monitoring, and maintenance of ML models.

Examples

No data.