Layer goes Open-source, ML in dbt DAG

Today, Layer goes open-source to make machine learning more accessible and contribute to ML's growth and evolution. Machine Learning is becoming the default way to build technology. It's how you make your apps smarter, your systems more reliable and your businesses smarter.

Layer goes Open-source, ML in dbt DAG
Experiment tracking with Layer: Layer Newsletter Issue 1

The last 2 weeks have been full of updates for Layer. I'm sure you will find something valuable for your own business.

Let's go!

⭐ Layer goes open-source!

Today, Layer goes open-source to make machine learning more accessible and contribute to ML's growth and evolution.

Machine Learning is becoming the default way to build technology. It's how you make your apps smarter, your systems more reliable and your businesses smarter. This is mostly possible by the open-science efforts; from open-source ML frameworks to open datasets.

We will open-source more including our roadmap. Meanwhile, check out our repo, and don't forget to give us a star!

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πŸ’« Layer is listed on official dbt adapters!

Today, we announce our open-source dbt Adapter which runs ML pipelines inside dbt DAG. With Layer dbt Adapter you can:

To immediately start using Layer inside your dbt DAG, install the Layer dbt Adapter for BigQuery:

pip install dbt-layer-bigquery -U

You can then automatically build state-of-the-art ML models using your own dbt models with plain SQL.

 

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🌐 Track ML models using Superwise and Layer!

We have partnered with Superwise to ensure that your models never drift again. In this article, we illustrate how you can use Layer and Amazon SageMaker to deploy a machine learning model and track it using Superwise. 

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For more machine learning news, tutorials, code, and discussions join us on SlackTwitter, and GitHub.

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