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Description
DIA (Decentralised Information Asset) serves as an open-source oracle framework that facilitates the sourcing, supplying, and sharing of reliable data among market participants. In the decentralized finance (DeFi) sector, access to trustworthy and scalable data feeds is crucial for developing dependable products and protecting against potential exploitation and manipulation. By utilizing crypto-economic incentives and community insights, DIA effectively sources, validates, and disseminates trusted financial information. The platform rewards participants who contribute to data sourcing and validation through bounties funded by DIA tokens. All information is gathered from primary sources and directed to DIA's servers, where the database is hashed on-chain for security. Additionally, all relevant scraper code and documentation are made available on GitHub. Users can access this data through API endpoints or Oracles, enabling lending platforms, index providers, prediction markets, and others to tap into DIA’s open-source and validated data streams freely. This collaborative approach not only enhances data integrity but also fosters innovation within the DeFi ecosystem.
Description
An open-source platform for monitoring machine learning models offers robust observability features. It allows users to evaluate, test, and oversee models throughout their journey from validation to deployment. Catering to a range of data types, from tabular formats to natural language processing and large language models, it is designed with both data scientists and ML engineers in mind. This tool provides everything necessary for the reliable operation of ML systems in a production environment. You can begin with straightforward ad hoc checks and progressively expand to a comprehensive monitoring solution. All functionalities are integrated into a single platform, featuring a uniform API and consistent metrics. The design prioritizes usability, aesthetics, and the ability to share insights easily. Users gain an in-depth perspective on data quality and model performance, facilitating exploration and troubleshooting. Setting up takes just a minute, allowing for immediate testing prior to deployment, validation in live environments, and checks during each model update. The platform also eliminates the hassle of manual configuration by automatically generating test scenarios based on a reference dataset. It enables users to keep an eye on every facet of their data, models, and testing outcomes. By proactively identifying and addressing issues with production models, it ensures sustained optimal performance and fosters ongoing enhancements. Additionally, the tool's versatility makes it suitable for teams of any size, enabling collaborative efforts in maintaining high-quality ML systems.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$500 per month
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
DIA Association
Founded
2018
Country
Switzerland
Website
diadata.org
Vendor Details
Company Name
Evidently AI
Founded
2020
Country
United States
Website
www.evidentlyai.com
Product Features
Product Features
Data Quality
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization