Name: BentoML
Author: BentoML

Help Desk

Web and Video Conferencing

Customer Support

Agile Development

Image Recognition

Development

Academic Advising

Education

Business Intelligence (BI)

Collaboration

Contract Management

Streaming Analytics

Supply Chain Management

Enterprise

Accounting

Cash Flow Management

Crypto Asset Management

Investor Relations

Finance and Accounting

Applicant Tracking

Corporate Learning Management

HR Management

Payroll

Talent Intelligence

Talent Management

Technical Skills Assessment

Workforce Analytics

Workforce Management

Human Resources

Cloud Storage

Data Integration

Data Mapping

Debugging

Edge Computing

Event Monitoring

Fraud Detection

IT Asset Disposal

Integration Platform as a Service (iPaaS)

Internet Security

License Management

Managed Hosting

Master Data Management (MDM)

Network Diagnostics

Network Performance Monitoring

NoSQL Databases

Packet Analyzer

Security Analytics

Storage Management

Tape Storage

Unified Endpoint Management (UEM)

WAN Optimization

Web Content Filtering

Wireless LAN

Wireless WAN

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Community

Content Management

Digital Rewards

Email Marketing

Marketing Analytics

Marketing Automation

Predictive Analytics

Social Media Analytics

Social Media Management

Survey & Forms Building

Video Collaboration

Web Analytics

Marketing

Project Management

Professional Services

Customer Communication Management

Customer Relationship Management (CRM)

Lead-to-Account Matching and Routing

Sales

Microsegmentation

Security

Animal Shelter

Animation

Computer-Aided Design (CAD)

Construction Management

Debt Collection

Tax Practice Management

Vertical-Specific

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BentoML is a framework designed to address the needs of building reliable, scalable, and cost-efficient AI applications, according to the vendor. It provides a standardized and user-friendly platform for packaging AI applications, allowing users to easily manage and deploy their AI apps. BentoML supports flexible model compatibility, enabling users to import models from any model hub or bring their own models built with popular frameworks like PyTorch, TensorFlow, Keras, Scikit-Learn, and XGBoost. The vendor states that BentoML simplifies the process of creating API servers for AI models by supporting auto-generation of REST API, gRPC, and long-running inference jobs. Additionally, BentoML automates the generation of Docker container images, making it effortless to deploy applications in any Docker-supported environment. 

According to the vendor, BentoML adopts a Python-first approach, making it easy to scale complex AI workloads. Users can run and debug their BentoML apps locally on Mac, Windows, or Linux, ensuring a smooth development experience. The vendor claims that BentoML eliminates the complexities of GPU inference, allowing users to effortlessly take advantage of GPU acceleration for optimal performance. BentoML enables users to compose multiple models and run them concurrently or sequentially, leveraging multiple GPUs or a Kubernetes cluster, as per the vendor.

BentoML natively integrates with popular ML platforms such as MLFlow, LangChain, Kubeflow, Triton, Spark, and Ray, according to the vendor. This integration completes the production AI stack, providing a comprehensive solution for AI model deployment.

BentoML is an open-source platform for application developers used to build, ship, and scale AI applications. It supports model serving, application packaging, and production deployment, and is free and open source under an Apache 2.0 license. And the BentoCloud version is a fully managed platform for building and operating AI applications, bringing agile product delivery to AI teams.

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