Run `modelbit.deploy()` from your Jupyter Notebook to deploy your ML model to production. Automatically get REST and Snowflake inference endpoints. Version control, CI/CD, logging, containerization, pipelines and feature stores come built-in.
Congratulations on the launch of Modelbit, your new project! As experienced makers of data tools, it's inspiring to see your decade-long commitment in this field, starting from Periscope Data and now venturing into Modelbit. The struggle faced by data scientists when deploying models has been acknowledged, and Modelbit aims to provide a solution.
With just a single command, modelbit.deploy(), Modelbit simplifies the deployment process from any Python notebook. It offers a wide range of functionalities, such as containerizing the running Python environment, shipping models and containers to the cloud, and providing REST endpoints and Snowflake SQL functions for inference. Version control in Git repositories, load balancing, logging, unit testing, retraining pipelines, A/B testing, and feature stores are also incorporated.
Your dedication to supporting data teams is evident, and Modelbit is poised to be an invaluable resource for data scientists. Congratulations once again on this remarkable achievement, and we wish you great success with Modelbit!
Very glad to work with you guys!
Being able to get the support we need, almost real-time, and push some of the complexity of ML deploying to you (sorry, not sorry) unblocks our model building/deployment by a LOT. 😁
We've been Modelbit users for a while now and love the product and the team. Here's an example of what we've built:
https://medium.com/building-inve...
