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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Alibaba Cloud Yes 
Bloomberg No 
Docker No 
Gojek No 
IBM Cloud No 
Kubeflow No 
Kubernetes No 
NAVER No 
NVIDIA DRIVE No 
ZenML No 
Zillow No 
vLLM No 

Integrations

Alibaba Cloud No 
Bloomberg Yes 
Docker Yes 
Gojek Yes 
IBM Cloud Yes 
Kubeflow Yes 
Kubernetes Yes 
NAVER Yes 
NVIDIA DRIVE Yes 
ZenML Yes 
Zillow Yes 
vLLM Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook 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 

Customer Support

Business Hours Yes 
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 Yes 
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

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 No 
Application Integration No 
Data Stores No 
Developer Tooling No 
Orchestration No 
Reporting / Analytics No 
Serverless Computing No 
Storage No 

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 

Alternatives

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