What is Modelbit?
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.
Problem
Data scientists and engineers face complexities when transitioning machine learning models from a Jupyter Notebook development environment to a production environment. The challenges include dealing with REST and Snowflake inference endpoints, version control, CI/CD, logging, containerization, pipelines, and feature stores, which can be time-consuming and require specialized knowledge. The difficulties in deploying ML models to production efficiently and securely.
Solution
Modelbit is a cloud-based platform that simplifies the deployment of machine learning models to production from a Jupyter Notebook. By running modelbit.deploy() command, users can automatically get REST and Snowflake inference endpoints. The platform also provides version control, CI/CD, logging, containerization, pipelines, and feature stores built-in, facilitating a smoother transition of ML models to a scalable and manageable production environment.
Customers
Data scientists and machine learning engineers working in various industries that require quick and efficient deployment of machine learning models to production, including but not limited to, technology, finance, healthcare, and retail.
Unique Features
Automatic generation of REST and Snowflake inference endpoints from Jupyter Notebooks, comprehensive built-in features such as version control, CI/CD, logging, containerization, pipelines, and feature stores, which distinguish Modelbit from conventional deployment solutions.
User Comments
Users generally perceive Modelbit as a groundbreaking tool that significantly eases the deployment process of ML models.
Appreciation for the ease of transitioning from Jupyter Notebooks to production.
Positive comments on the inclusion of built-in features like version control and CI/CD.
Some users express desire for more granular control over certain features.
Overall, feedback is highly positive, with users recommending Modelbit for its efficiency and convenience.
Traction
Information on Modelbit's specific traction metrics such as number of users, MRR (or ARR)/revenue, or financing is not readily available as of the knowledge cutoff in April 2023.
Market Size
Information on the specific market size for ML model deployment platforms is not readily available. However, the global machine learning market size is expected to grow from $1 billion in 2016 to $8.8 billion by 2022.
