Modelbit is excited to announce new functionality that makes it easier than ever for data science and machine learning teams to both deploy and manage ML models directly in Snowpark.
We are excited to announce that Tecton and Modelbit have partnered to release an integration to enable a more streamlined ML model deployment and feature management workflow.
Many modern model technologies require GPUs for training and inference. By using Modelbit alongside Hex, we can leverage Modelbit’s scalable compute with on-demand GPUs to do the model training. We can orchestrate the model training and deployment in our Hex project. And finally, we can deploy the model to a production container behind a REST API using Modelbit.
We are excited to announce that Neptune and Modelbit have partnered to release an integration to enable better ML model deployment and experiment tracking.
The new integration between Modelbit and Weights & Biases allows ML practitioners to train and deploy their models in Modelbit while logging and visualizing training progress in Weights & Biases.
Modelbit and Arize’s new integration enables teams to rapidly deploy ML models into production with one line of code and begin monitoring and fine tuning instantly.
Announcing the Eppo & Modelbit Partnership! Learn how to A/B test your machine learning models using the two premier MLOps platforms.
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