Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Outline your cloud-native infrastructure and manage it as a systematic approach. Create a configuration for your service mesh alongside the deployment of workloads. Implement smart canary strategies and performance profiles while managing the service mesh pattern. Evaluate your service mesh setup based on deployment and operational best practices utilizing Meshery's configuration validator. Check the compliance of your service mesh with the Service Mesh Interface (SMI) standards. Enable dynamic loading and management of custom WebAssembly filters within Envoy-based service meshes. Service mesh adapters are responsible for provisioning, configuration, and management of their associated service meshes. By adhering to these guidelines, you can ensure a robust and efficient service mesh architecture.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Docker
Yes
Kubernetes
Yes
Bloomberg
Yes
Cilium
No
F5 NGINX Gateway Fabric
No
Gojek
Yes
Google Kubernetes Engine (GKE)
No
HashiCorp Consul
No
Helm
No
IBM Cloud
Yes
Integrations
Docker
Yes
Kubernetes
Yes
Bloomberg
No
Cilium
Yes
F5 NGINX Gateway Fabric
Yes
Gojek
No
Google Kubernetes Engine (GKE)
Yes
HashiCorp Consul
Yes
Helm
Yes
IBM Cloud
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
KServe
Website
kserve.github.io/website/latest/
Vendor Details
Company Name
Meshery
Website
meshery.io
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No