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Modelbit

Claim Tool

Last updated: August 8, 2024

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1 reviews

What is Modelbit?

Modelbit enables you to deploy ML models from any Python environment and infer from a range of data sources including Snowflake, Redshift, dbt, and REST APIs. It's backed by your git repository for robust version control, CI/CD, and code review. The platform also includes on-demand GPUs for training any custom ML model and offers extensive logging and monitoring features for enhanced observability. Deploy, scale, and manage your models seamlessly in your own cloud or Modelbit's.

Category

Machine Learning

Modelbit's Top Features

Deploy from any Python environment

On-demand GPUs for training

Infer from Snowflake, Redshift, dbt, REST APIs

Backed by git repo for version control, CI/CD, code review

Robust logging and monitoring

Deploy in your cloud or Modelbit's

Built-in tools for MLOps

Support for custom and open-source models

Automated CI/CD

Comprehensive observability and alert systems

Frequently asked questions about Modelbit

Modelbit's pricing

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    Use Cases

    Data Scientists

    Deploy ML models directly from Jupyter, Hex, Deepnote, VS Code, and other Python environments.

    MLOps Engineers

    Utilize robust logging, monitoring, and alert systems for better observability and reliability of machine learning models.

    Machine Learning Engineers

    Train custom ML models using on-demand GPUs for instant compute resources.

    Software Developers

    Integrate Modelbit into existing git-based version control systems for seamless CI/CD processes.

    Data Engineers

    Infer from a wide range of data sources including Snowflake, Redshift, dbt, and REST APIs.

    Product Managers

    Schedule demos and gather detailed insights to better understand the deployment and monitoring processes.

    Tech Leads

    Deploy, scale, and manage ML models seamlessly either in their own cloud or Modelbit's infrastructure.

    AI Researchers

    Deploy both custom and open-source ML models for research and experimentation.

    Business Analysts

    Use deployment and inferencing capabilities to integrate advanced ML models into business workflows.

    IT Administrators

    Manage comprehensive security, logging, and monitoring of ML models deployed in the cloud.

    Explore Top AI Use Cases