Evidently AI 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.

Pricing

Pricing Starts At:
$500 per month
Free Version:
Yes

Integrations

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Company Details

Company:
Evidently AI
Year Founded:
2020
Headquarters:
United States
Website:
www.evidentlyai.com

Media

Evidently AI Screenshot 1
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Product Details

Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support

Evidently AI Features and Options

Machine Learning Software

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Natural Language Processing Software

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

Data Quality Software

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

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