Modelbit

Reilly Thiel

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!

10 months ago

Roy Kris

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!

1 year ago

Daren Bruen

I already have a bit of context about Modelbit (we invested at Weekend Fund) but I'm curious to ask a questions somewhat unrelated to this launch... @harry_glaser – You and Tom previously built and exited Periscope Data for over $100M. You know how hard it is to build a large company. Why do it again? Isn't it easier to just rest and vest at a large co. 😅

1 year ago

Roy Kris

thrilled to see you launch publicly!

1 year ago

Monserrate VonRueden

Congrats on the launch! I was wondering, do you support any warehouses beyond snowflake?

1 year ago

Jake Blanda

The experience for data scientists is amazing (permissionless starts, one-line deploys, etc). I love the “creativity loves constraints” philosophy you’ve adopted that rules out the massive adoption, usage, and maintenance headaches associated with heavy-duty ML platforms.

1 year ago

Haleigh Bartoletti

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! ❤

1 year ago

Hillard Halvorson

After building many ML centric products over the years, I can attest that getting a model into production is one of the hardest problems in software. It's so hard that it often slows down product development to a crawl, as the Data Scientists don't have the skills to move a model into production but the engineers don't understand how the models work. ModelBit is addressing this head on and I wish we had it when I was still building products!

1 year ago

Eino Klein

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. 😁

1 year ago

Lamont Strosin

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: <a href="https://medium.com/building-inventa/how-we-use-ml-to-reduce-predicted-shipping-times-by-66-a6f9ee2b1c1c" target="_blank" rel="nofollow noopener noreferrer">https://medium.com/building-inve...</a>

1 year ago

Scotty Feest

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&nbsp;with&nbsp;Modelbit!

1 year ago