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How To Build an
ML Platform from
Scratch

Your data science team is growing, meaning more models are heading to production. This calls for a standard way to build, train, serve, deploy, and monitor your models. While this may be a given – how you build this ML infrastructure is still a long and winding road, and how you monitor your platform is still up in the air.

Different approaches call for different methods, and this ebook will conveniently break down how to build an ML platform, using popular open-source tools. You’ll also gain valuable insights into leveraging Aporia’s customizable ML monitoring to ensure your model is driving its intended value.

In this guide, you'll learn

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Building an ML platform using
open-source tools – DVC,
Cookiecutter, MLFlow, FastAPI

Building an ML platform using open-source tools – DVC, Cookiecutter, MLFlow, FastAPI

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Teach you the basic principles
of how to build your own ML
platform

Teach you the basic principles of how to build your own ML platform

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Cloud Native DevOps
Infrastructure for ML Platforms -
Kubernetes, Helm, Pulumi, Poetry

Cloud Native DevOps Infrastructure for ML Platforms - Kubernetes, Helm, Pulumi, Poetry

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Monitoring your ML models with
Aporia, an important step to putting
Responsible AI into practice.

Monitoring your ML models with Aporia, an important step to putting Responsible AI into practice.

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