Modelbit is proud to announce $5M of seed funding led by Leo Polovets of Susa Ventures, with participation from Snowflake and other funds and angel investors.
Five tricks we've learned the hard way to make working with Pandas DataFrames easier for data scientists everywhere.
How to call lambda functions efficiently in batch from Amazon Redshift. A helpful guide for those struggling with slow calls to Lambda from Redshift when doing batch inference for machine learning.
A simple and elegant way to develop machine learning models in Hex, and then deploy them to the cloud with Modelbit
How to build a lead scoring model for a b2b business and deploy it to production so it can be used in both online inference via REST API, and offline batch scoring via SQL function.
A step-by-step guide to building ML models in Deepnote and deploying them to Snowflake using Modelbit.
Our first impressions of Snowpark Python, Snowflake's new arbitrary compute environment for Python!
With Modelbit, running training jobs in the cloud is easy! Learn how to use our Python API to run training jobs in the cloud without leaving your Jupyter notebook.
Your ML model is in a lambda function in the data science AWS account. The Redshift cluster is in the engineering AWS account. You want to call your models to make your predictions in AWS Redshift. What to do?! A guide to cross-account calls from Redshift to Lambda.
For years we've struggled to get our ML models out of our Jupyter notebooks and into production cloud environments. Finally we built a solution. Here's how it works.
