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Building an ML Platform

The shared infrastructure that lets teams build, deploy and monitor models consistently, and how to grow it.

Editorial team 1 min read

As organisations run more models, shared platforms prevent every team from rebuilding the same infrastructure.

Typical Components

  • Data access and feature pipelines.
  • Experiment tracking.
  • Training infrastructure with managed compute.
  • Model registry.
  • Deployment and serving.
  • Monitoring and alerting.
  • LLM gateway for model API access, logging and cost control.

Benefits

  • Faster delivery for teams.
  • Consistent governance, security and monitoring.
  • Lower costs through shared resources.

Build Versus Buy

Managed cloud ML platforms and open-source tools cover much of this. Build only what differentiates you.

Platform as a Product

  • Understand users' needs.
  • Provide good documentation and templates.
  • Make the standard path the easiest path.
  • Measure adoption and satisfaction.

Start Small

Standardise the most painful steps first — often deployment and monitoring — then expand.

Avoid Over-Engineering

Match platform investment to the number and importance of models you run.

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