The Monitoring Platform for AI-Driven Enterprises.
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What We Do
High-Performance Machine Learning Models at Scale.
Viewing your data like never before – from multiple points of view, over very long and periods of time, across high-volume sets of dimensions.
Cutting through the noise to identify alerts that matter, and fix the important problems.
Spotting patterns that influence model behavior deep in segments and sub-segments of data - at very high speed.
Running on any cloud and integrating with any data source.
Monitoring
Experience the most comprehensive risk monitoring, with massive reach and depth manage the most complex, high volume models.
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Root-Cause Analysis
Perform on-the-fly analysis across massive volumes of data and drill deep into segments to understand what is happening.
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Validation
Ensure models perform as expected and automate redeployment workflows – without disrupting business.
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MITIGATION
Understand what action to take and trigger automated workflows to retrain models when needed.
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Scale
What does scale look like in your organization?
Today, scale is about real-time, on-the-fly, drill-down analytics on input data and output data on millions or billions of data points. It’s about getting highly granular, detailed insight into problems that jeopardize model reliability. Anytime – as needed. Scale may mean thousands of models in production, or a few complex models with thousands of features, and it encompasses the people and processes needed to operationalize ML across the enterprise. VIANOPS is the purpose built platform designed to support scalability across a large number of models, features, segments & volumes of data in a cost-efficient way.
Observe input data and feature drift using an infinite number of highly flexible dimensions to meet business needs – e.g., day to day, quarter to quarter for past 3 years, week-to-date to last year same week.
Identify and track features that have the most impact on model behavior, even on highly complex models with hundreds or thousands of features.
Perform ad-hoc, real-time analysis on tens of millions or even billions of data points to find understand why an error occurs or another type of expected result.
Operating at very high scale and very low cost.
Keeping your models trustworthy just got so much easier.
VIANOPS delivers a closed-loop of continuous operations for models in production with the most comprehensive set of risk monitoring capabilities, giving users actionable insights to perform root cause analysis, retraining recommendations, and the ability to validate model performance before redeployment. And VIANOPS does all this at massive scale.
Best in class UX
Highly flexible and customizable alert settings
Very high scale - at lower infrastructure cost
A New Approach.
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With VIANOPS, we fundamentally change the approach to be a comprehensive, high-scale, and flexible approach that puts the power into the hands of ML operations teams that need to drive high-performance machine learning models at scale, to support reliable business outcomes.
Experience a faster, streamlined path to operationalizing your models, and keep them delivering business value longer. Now it’s easy to:
Quickly determine which alerts matter
Get actionable, unambiguous metrics to answer specific questions about model performance
Know what to do next
Operationalize your ML workflow with VIANOPS continuous ML operations – without disrupting business operations.
Streamline Your ML Workflow.
Data Scientists
Proactively Manage & Analyze Model Performance
- Observe drift from multiple perspectives
- Correlate feature drift to prediction drift
- Slice data into segments to uncover patterns and better understand model behavior
Business Users
Explore Model Policies and Metrics Understand Model Behavior
- Easily visualize model performance with visual charts and color coded alerts
- Communicate business impact with meaningful metrics
- Operate at scale, while maintaining lower infrastructure cost
ML Engineers
Identify Performance Issues Quickly
- Rapidly respond to critical alerts while managing warnings proactively
- Explore drift from multiple perspectives and perform data quality checks
- Collaborate with other stakeholders to analyze model performance
ML Operational Excellence.
White-paper
Keeping models trustworthy after they go into production is hard as model behavior changes when acting on real world data. Teams need to know why models degrade, how to identify root cause, and what actions to take to remediate the issues. This needs to happen at scale, as more models are put into production and as models become increasingly complex, with more features and more inferences. Our next generation platform for model monitoring and continuous operations is the backbone for reliable, responsible, transparent, trustworthy, and cost-effective human-centered AI within an enterprise.
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We work with a wide variety of industries & users.
VIANOPS was designed for extreme scalability to support the demands of banking, oil & energy, manufacturing, and other enterprise organizations. Using feature drift analysis as an example, VIANOPS quantization of baseline windows enables users to identify root cause across long periods of time – such as years – across models with hundreds or thousands of features.
View billions of data points in seconds – on commodity hardware
Find pattern shifts and anomalies in real-time, in massive amounts of production data
“The breakthrough for us is the sheer scale at which we can now monitor our models, with customized monitoring plans across a very large number of features, using Vianai. This includes hundreds of thousands of events per second that we can monitor for potential risks around accuracy, drift and other issues very quickly, and then automatically retrain models if needed.”