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Description
KServe is a robust model inference platform on Kubernetes that emphasizes high scalability and adherence to standards, making it ideal for trusted AI applications. This platform is tailored for scenarios requiring significant scalability and delivers a consistent and efficient inference protocol compatible with various machine learning frameworks. It supports contemporary serverless inference workloads, equipped with autoscaling features that can even scale to zero when utilizing GPU resources. Through the innovative ModelMesh architecture, KServe ensures exceptional scalability, optimized density packing, and smart routing capabilities. Moreover, it offers straightforward and modular deployment options for machine learning in production, encompassing prediction, pre/post-processing, monitoring, and explainability. Advanced deployment strategies, including canary rollouts, experimentation, ensembles, and transformers, can also be implemented. ModelMesh plays a crucial role by dynamically managing the loading and unloading of AI models in memory, achieving a balance between user responsiveness and the computational demands placed on resources. This flexibility allows organizations to adapt their ML serving strategies to meet changing needs efficiently.
Description
Transform your Kubernetes autoscaling from a reactive approach to a proactive one with PredictKube, enabling you to initiate autoscaling processes ahead of anticipated load increases through our advanced AI predictions. By leveraging data over a two-week period, our AI model generates accurate forecasts that facilitate timely autoscaling decisions. The innovative predictive KEDA scaler, known as PredictKube, streamlines the autoscaling process, reducing the need for tedious manual configurations and enhancing overall performance. Crafted using cutting-edge Kubernetes and AI technologies, our KEDA scaler allows you to input data for more than a week and achieve proactive autoscaling with a forward-looking capacity of up to six hours based on AI-derived insights. The optimal scaling moments are identified by our trained AI, which meticulously examines your historical data and can incorporate various custom and public business metrics that influence traffic fluctuations. Furthermore, we offer free API access, ensuring that all users can utilize essential features for effective autoscaling. This combination of predictive capabilities and ease of use is designed to empower your Kubernetes management and enhance system efficiency.
API Access
Has API
API Access
Has API
Integrations
Kubeflow
Kubernetes
Amazon Web Services (AWS)
Bloomberg
Docker
Gojek
Google Cloud Platform
IBM Cloud
Kuna Exchange
Microsoft Azure
Integrations
Kubeflow
Kubernetes
Amazon Web Services (AWS)
Bloomberg
Docker
Gojek
Google Cloud Platform
IBM Cloud
Kuna Exchange
Microsoft Azure
Pricing Details
Free
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
KServe
Website
kserve.github.io/website/latest/
Vendor Details
Company Name
PredictKube
Country
United States
Website
predictkube.com
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization