Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Founded in 2016, Infervision stands at the forefront of AI-driven medical technology, dedicated to enhancing multidisciplinary healthcare services and operations through innovative artificial intelligence solutions. Their advanced AI offerings are designed to assist healthcare professionals across a spectrum of tasks, including disease screening, diagnosis, intervention, treatment, patient management, and medical research. Among their array of solutions, InferRead CT Lung specializes in detecting lung nodules from chest CT scans, while InferRead DR Chest focuses on identifying chest abnormalities via X-ray imaging. Moreover, InferRead CT Coronary is tailored for recognizing coronary artery stenosis during coronary CT angiography, and InferRead CT Stroke is adept at triaging hemorrhages from brain CT scans. The company also provides InferRead CT Bone for spotting chest fractures in CT images and InferRead CT Pneumonia for diagnosing and managing pneumonia cases. Beyond these, their offerings extend to InferOperate, which facilitates 3D reconstruction for thoracic, liver, and urological surgical procedures, and InferCare, a tool designed for effective patient management and image follow-up. Additionally, InferScholar acts as an AI-enhanced resource for medical research, further solidifying Infervision's commitment to transforming healthcare through AI technology. This comprehensive suite of solutions positions Infervision as a pivotal player in the evolution of medical practices.
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
Integrations
Bloomberg
No
Docker
No
Gojek
No
IBM Cloud
No
Kubeflow
No
Kubernetes
No
NAVER
No
NVIDIA DRIVE
No
ZenML
No
Zillow
No
Integrations
Bloomberg
Yes
Docker
Yes
Gojek
Yes
IBM Cloud
Yes
Kubeflow
Yes
Kubernetes
Yes
NAVER
Yes
NVIDIA DRIVE
Yes
ZenML
Yes
Zillow
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
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)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Infervision
Founded
2016
Country
United States
Website
global.infervision.com
Vendor Details
Company Name
KServe
Website
kserve.github.io/website/latest/
Product Features
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