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Average Ratings 0 Ratings
Description
The initial step in your process should always be modeling your data, as applications may come and go, but data remains constant. After successfully implementing your model, your CubicWeb application will operate, allowing you to gradually introduce valuable features for your users. RQL, which is based on your application model, is a concise language that emphasizes the attributes and connections inherent in the data. While it shares similarities with SPARQL, RQL is generally more user-friendly. Once a RQL query retrieves a data graph, various views can be applied to present the information in the most pertinent format. This design principle is fundamental to the entire CubicWeb architecture. Permissions are intricately defined within the data model, allowing for exceptional precision. Furthermore, any RQL query made to the engine automatically undergoes security checks to ensure safe handling. CubicWeb utilizes a conventional SQL database for data storage and management, with PostgreSQL being the favored choice among its users. By leveraging these capabilities, CubicWeb not only enhances functionality but also prioritizes security and data integrity.
Description
PostgresML serves as a comprehensive platform integrated within a PostgreSQL extension, allowing users to construct models that are not only simpler and faster but also more scalable directly within their database environment. Users can delve into the SDK and utilize open-source models available in our hosted database for experimentation. The platform enables a seamless automation of the entire process, from generating embeddings to indexing and querying, which facilitates the creation of efficient knowledge-based chatbots. By utilizing various natural language processing and machine learning techniques, including vector search and personalized embeddings, users can enhance their search capabilities significantly. Additionally, it empowers businesses to analyze historical data through time series forecasting, thereby unearthing vital insights. With the capability to develop both statistical and predictive models, users can harness the full potential of SQL alongside numerous regression algorithms. The integration of machine learning at the database level allows for quicker result retrieval and more effective fraud detection. By abstracting the complexities of data management throughout the machine learning and AI lifecycle, PostgresML permits users to execute machine learning and large language models directly on a PostgreSQL database, making it a robust tool for data-driven decision-making. Ultimately, this innovative approach streamlines processes and fosters a more efficient use of data resources.
API Access
Has API
API Access
Has API
Integrations
Python
Amazon EC2
Codestral Mamba
Falcon
Jupyter Notebook
Llama
Llama 2
Llama 3
Llama 3.1
Llama 3.3
Integrations
Python
Amazon EC2
Codestral Mamba
Falcon
Jupyter Notebook
Llama
Llama 2
Llama 3
Llama 3.1
Llama 3.3
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$.60 per hour
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
CubicWeb
Website
www.cubicweb.org
Vendor Details
Company Name
PostgresML
Website
postgresml.org