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 to you and Tom on the launch of Modelbit! 🎉🚀 Your passion for helping data scientists deploy models shines through in this impressive project. By simplifying the deployment process with just a single command, modelbit.deploy(), you are leveling the playing field for data scientists outside of Big Tech companies. The comprehensive features of Modelbit, from containerization and cloud deployment to REST endpoints and version control, demonstrate your dedication to supporting data teams. We are confident that this powerful tool will be invaluable for data scientists and greatly enhance their productivity. Congratulations once again, and we wish you tremendous success with Modelbit! ❤
Congrats on building a great product for data scientists to deploy to production with one line of code, and on building a company culture that is inclusive and flexible. I’m excited to be a decade-long investor in Tom and Harry first in Periscope and for the past year in Modelbits. Let’s go!
Congrats on the launch!
I was wondering, do you support any warehouses beyond snowflake?
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Running modelbit.deploy() from your Jupyter Notebook is a total game-changer for ML model deployment! With just a single command, you can effortlessly deploy your model to production and unlock REST and Snowflake inference endpoints. The built-in features, including version control, CI/CD, logging, containerization, pipelines, and feature stores, make it an all-in-one solution for seamless deployment. Say goodbye to complexity and hello to hassle-free ML model deployment with ease!
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