Vian H+AI MLOps Platform

Trustworthy, Optimized, Enterprise-Wide Deployment and Management of Machine Learning Models at Scale.

Vian H+AI MLOps Platform Overview

The Vian H+AI MLOps Platform delivers a simplified user experience and modular approach to build, optimize, deploy, manage and govern ML models at scale. Customers can use the entire end-to-end platform, or simply plug and play key components seamlessly, to create a customized solution. Our Vian ML Model Monitoring capabilities enable customers to monitor hundreds or thousands of models including mission-critical models with extremely high transaction volumes.

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Categories:
All
MLOps
Data
Data+MLOps

System-wide Features:
Teamwork and Collaboration
Enterprise Security
Governance
Enterprise Support

MLOps Features:
Data Acquisition
Data Versioning
Data Visualization
Data Preparation
Data Pipelines
Data Labeling
AutoML
Featurization
Feature Store
ML Pipelines or Workflows
Model Registry
Model Marketplace
Model Training
Distributed Model Training
Model Debugging
Experiment Management
Deep Learning Support
Reinforcement Learning Support
Bias Detection and Mitigation
Model Explainability
Hyperparameter Optimization
Model Packaging
Model Deployment and Serving
Edge ML Support
Model Monitoring
Cost Management
ML Infrastructure Orchestration
Accelerator Support
Kubernetes Support

Model Deployment Sub-features:
Model serving
Integrates with CI/CD pipeline
Roll-back options
Optimizes Infrastructure Dynamically
Auto-scales Infrastructure
Supports Batch Inference
Supports Streaming Inference

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Additional Product Information

Product Links

Website

Deploys On

Amazon Web Services
Google Cloud Platform
Microsoft Azure
Other Public Cloud
Kubernetes
NVIDIA
Private Cloud or Datacenter
SaaS

Pricing:

Free Version

Paid Version

Vianai Vendor Information

Vendor Overview

Our platform combines open-source elements, Vianai-proprietary techniques and optimizations, and human-centered design to bring AI to the enterprise at scale, across diverse landscapes. With our platform, large organizations can build, optimize, deploy and manage sophisticated ML models on existing infrastructure and improve the operations and performance of ML models across the enterprise.