ML Platform:
Build it 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 find everything you need to build an ML platform from scratch, including:

ML Platform:
Build it 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 find everything you need to build an ML platform from scratch, including:

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

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

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Monitoring your ML models with Aporia.

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