Predictive analytics on relational data? getML 1.5 is here!

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After 9 months of development, we are releasing getML 1.5 into the world. This is whats new.

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1️⃣ MkDocs-based-everything: We consolidated homepage and documentation both to MkDocs for a better experience on both ends – for us building it and everyone else consuming it. Check it out at https://getml.com/latest. Let us know if anything’s missing!

2️⃣ getML for experimentation: Our Docker Compose setup makes running getML on any system and environment a two line task – see our git https://github.com/getml/getml-demo. Btw, anyone wish for nativ Windows support?

$ git clone https://github.com/getml/getml-demo.git $ docker compose up notebooks

3️⃣ Performance Boost: A redesigned IO stack between our engine and python API, powered by PyArrow, improves speed and reliability.

4️⃣ Smarter Pipelines Strict typing for feature aggregations and loss functions enhances reliability.

5️⃣ Colab Compatibility: Deploy all getML-community notebooks to Google Colab with a single click https://getml.com/latest/examples/community-notebooks/

Great! But why is getML essential?

Relational and time-series data are the backbone of modern applications, from customer analytics to predictive maintenance. getML is the fastest software for feature engineering reducing code and costs associated with manual feature engineering by up to 90%!

How will getML 1.5 accelerate your data science work? Let us know! 📩