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Description
Distributed AI represents a computing approach that eliminates the necessity of transferring large data sets, enabling data analysis directly at its origin. Developed by IBM Research, the Distributed AI APIs consist of a suite of RESTful web services equipped with data and AI algorithms tailored for AI applications in hybrid cloud, edge, and distributed computing scenarios. Each API within the Distributed AI framework tackles the unique challenges associated with deploying AI technologies in such environments. Notably, these APIs do not concentrate on fundamental aspects of establishing and implementing AI workflows, such as model training or serving. Instead, developers can utilize their preferred open-source libraries like TensorFlow or PyTorch for these tasks. Afterward, you can encapsulate your application, which includes the entire AI pipeline, into containers for deployment at various distributed sites. Additionally, leveraging container orchestration tools like Kubernetes or OpenShift can greatly enhance the automation of the deployment process, ensuring efficiency and scalability in managing distributed AI applications. This innovative approach ultimately streamlines the integration of AI into diverse infrastructures, fostering smarter solutions.
Description
Incorporate our advanced text analytics APIs to infuse your product, platform, or application with state-of-the-art natural language processing capabilities. Boasting the most comprehensive NLP feature set available, our technology has been refined over 19 years and is continually updated with new libraries, configurations, and models. You can assess whether a written piece conveys a positive, negative, or neutral sentiment, as well as sort and categorize documents into tailored groups. Additionally, our system can identify the expressed intentions of customers and reviewers, and extract pertinent information such as people, locations, dates, companies, products, jobs, and titles. You have the flexibility to deploy our text analytics and NLP solutions across a variety of infrastructures, including on-premise, private cloud, hybrid cloud, and public cloud environments. Our foundational software libraries for text analytics and natural language processing are fully accessible and at your service. This offering is especially advantageous for data scientists and architects who seek unrestricted access to the core technology or require on-premise deployment to maintain security and privacy standards. Ultimately, our innovative solutions empower you to harness the full potential of language data effectively.
API Access
Has API
API Access
Has API
Integrations
.NET
Altair Activate
C#
CisionOne
DataSift
Hootsuite
Kubernetes
Microsoft 365
PubNub
PyTorch
Integrations
.NET
Altair Activate
C#
CisionOne
DataSift
Hootsuite
Kubernetes
Microsoft 365
PubNub
PyTorch
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
IBM
Country
United States
Website
developer.ibm.com/apis/catalog/edgeai--distributed-ai-apis/Introduction/
Vendor Details
Company Name
Lexalytics
Country
United States
Website
www.lexalytics.com
Product Features
Product Features
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
Text Mining
Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering