Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Sandboxed containers and virtual private cloud (VPC) configurations facilitate network isolation, thereby safeguarding application runtimes. SAE offers robust solutions geared towards high availability for large-scale events, which necessitate exact capacity management, extensive scalability, along with service throttling and degradation. Fully-managed Infrastructure as a Service (IaaS) utilizing Kubernetes clusters presents cost-effective options for businesses. SAE is capable of scaling in mere seconds while enhancing the efficiency of runtimes and expediting Java application initialization. The One-Stop Platform as a Service (PaaS) encompasses seamlessly integrated basic services, microservices, and DevOps tools. SAE also supports comprehensive lifecycle management for applications, allowing the implementation of various release strategies, including phased and canary releases. Furthermore, it accommodates a traffic-ratio-based canary release model, ensuring that the entire release process is fully observable and can be easily reverted if necessary. This comprehensive approach not only streamlines deployment but also enhances overall operational resilience.
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.
API Access
Has API
API Access
Has API
Integrations
Alibaba Cloud
Bloomberg
Docker
Gojek
IBM Cloud
Kubeflow
Kubernetes
NAVER
NVIDIA DRIVE
ZenML
Integrations
Alibaba Cloud
Bloomberg
Docker
Gojek
IBM Cloud
Kubeflow
Kubernetes
NAVER
NVIDIA DRIVE
ZenML
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
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
Alibaba Cloud
Founded
2008
Country
China
Website
www.alibabacloud.com/product/severless-application-engine
Vendor Details
Company Name
KServe
Website
kserve.github.io/website/latest/
Product Features
Serverless
API Proxy
Application Integration
Data Stores
Developer Tooling
Orchestration
Reporting / Analytics
Serverless Computing
Storage
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization