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What is KServe?

KServe stands out as a powerful model inference platform designed for Kubernetes, prioritizing extensive scalability and compliance with industry standards, which makes it particularly suited for reliable AI applications. This platform is specifically crafted for environments that demand high levels of scalability and offers a uniform and effective inference protocol that works seamlessly with multiple machine learning frameworks. It accommodates modern serverless inference tasks, featuring autoscaling capabilities that can even reduce to zero usage when GPU resources are inactive. Through its cutting-edge ModelMesh architecture, KServe guarantees remarkable scalability, efficient density packing, and intelligent routing functionalities. The platform also provides easy and modular deployment options for machine learning in production settings, covering areas such as prediction, pre/post-processing, monitoring, and explainability. In addition, it supports sophisticated deployment techniques such as canary rollouts, experimentation, ensembles, and transformers. ModelMesh is integral to the system, as it dynamically regulates the loading and unloading of AI models from memory, thus maintaining a balance between user interaction and resource utilization. This adaptability empowers organizations to refine their ML serving strategies to effectively respond to evolving requirements, ensuring that they can meet both current and future challenges in AI deployment.

What is JFrog ML?

JFrog ML, previously known as Qwak, serves as a robust MLOps platform that facilitates comprehensive management for the entire lifecycle of AI models, from development to deployment. This platform is designed to accommodate extensive AI applications, including large language models (LLMs), and features tools such as automated model retraining, continuous performance monitoring, and versatile deployment strategies. Additionally, it includes a centralized feature store that oversees the complete feature lifecycle and provides functionalities for data ingestion, processing, and transformation from diverse sources. JFrog ML aims to foster rapid experimentation and collaboration while supporting various AI and ML applications, making it a valuable resource for organizations seeking to optimize their AI processes effectively. By leveraging this platform, teams can significantly enhance their workflow efficiency and adapt more swiftly to the evolving demands of AI technology.

Media

Media

Integrations Supported

Amazon SageMaker
Apache Kafka
Bloomberg
Docker
Gojek
Google Cloud BigQuery
IBM Cloud
Kubeflow
Kubernetes
MongoDB
NAVER
NVIDIA DRIVE
PyTorch
Snowflake
VLLM
ZenML
Zillow

Integrations Supported

Amazon SageMaker
Apache Kafka
Bloomberg
Docker
Gojek
Google Cloud BigQuery
IBM Cloud
Kubeflow
Kubernetes
MongoDB
NAVER
NVIDIA DRIVE
PyTorch
Snowflake
VLLM
ZenML
Zillow

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

KServe

Company Website

kserve.github.io/website/latest/

Company Facts

Organization Name

JFrog

Date Founded

2008

Company Location

United States

Company Website

www.qwak.com

Categories and Features

Machine Learning

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

Categories and Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

DevOps

Approval Workflow
Dashboard
KPIs
Policy Management
Portfolio Management
Prioritization
Release Management
Timeline Management
Troubleshooting Reports

Machine Learning

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

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