mldb.ai: Powering Real-Time Machine Learning at Scale

mldb.ai is an open-source machine learning database that enables seamless real-time training and deployment of models through a RESTful API, offering a scalable, flexible solution for data scientists and developers. With support for SQL-like queries, schema-free operations, and integration with TensorFlow, it handles diverse data types—including numbers, text, and images—while supporting large-scale data processing across local, S3, and HDFS environments. Its ability to expose models as HTTP endpoints without extra deployment steps makes it ideal for rapid iteration and production use.

Key Features:

• Real-Time Model Training & Deployment: Train models and serve them instantly via RESTful API.
• RESTful API Access: Models are exposed as HTTP endpoints with no additional deployment required.
• SQL for Data Manipulation: Advanced querying capabilities with support for schema-free operation and millions of columns.
• Scalability: Fully utilizes RAM and CPU cores, enabling vertical scaling for high-performance workloads.
• High-Volume Data Processing: Capable of handling billions of data points on cost-effective hardware.
• Multi-Data Type Support: Handles supervised and unsupervised learning for numerical, textual, and image data.
• TensorFlow Integration: Supports importing and running TensorFlow graphs directly.
• Flexible Data I/O: Reads and writes data in text, CSV, and JSON formats, with compatibility for local storage, S3, and HDFS.
• Open-Source Community Edition: Available on GitHub under the Apache license, fostering transparency and collaboration.
• Python Wrapper (pymldb): Enables easy integration and scripting using Python.

Pricing: mlib.ai offers a Free tier through its open-source Community Edition on GitHub, with additional enterprise capabilities available via paid plans (exact pricing not specified).

Conclusion: mlib.ai is a powerful, open-source machine learning database that brings real-time model deployment and scalable data processing within reach of developers and data scientists, combining ease of use with advanced capabilities for both experimentation and production environments.