Modelbit
Last updated: August 8, 2024
Reviews
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.
Learn to use AI like a Pro
Get the latest AI workflows to boost your productivity and business performance, delivered weekly by expert consultants. Enjoy step-by-step guides, weekly Q&A sessions, and full access to our AI workflow archive.
Category
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
Share
Customer Reviews
Share your thoughts
If you've used this product, share your thoughts with other customers
Recent reviews
News
Top Modelbit Alternatives
Unlock the Full Potential of AI with AI/ML API
Serverless Infrastructure for Generative AI
Transforming AI Development with Lightning Speed
No-Code Machine Learning Made Easy with Heimdall
Automated Tiny ML Platform
Streamline Your Machine Learning Workflow with Azure Machine Learning
Build, Run, and Manage AI Models with IBM Watson Studio
Amazon SageMaker - Accelerate your machine learning lifecycle
Accelerate Data-Driven Decisions with Brewit AI
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.
Learn to use AI like a Pro
Get the latest AI workflows to boost your productivity and business performance, delivered weekly by expert consultants. Enjoy step-by-step guides, weekly Q&A sessions, and full access to our AI workflow archive.