Veritas Predictive Insights showcased at IDTechExpo

Jay Thomas IDtechExpo.png

 

 

Veritas Predictive Insights, a cloud based AI/ML Engine for Veritas Appliances, was premiered at IDTechExpo at Santa Clara on November 15, 2018, coinciding with the world wide launch of Veritas Predictive Insights.

I had the pleasure of sharing the announcement of Veritas Predictive Insights AI/ML at IDTechExpo in Santa Clara highlighting the benefits to customers such as:

  • Enhanced Availability: Minimizes unplannedPicture 1 Predictive Insights.png downtime by performing proactive monitoring of the appliance health, offering prescriptive recommendations and improving the overall resiliency of appliances.
  • Prescriptive Fault Resolution: Ensures faster time to fault resolution by using AI/ML based analytics to accurately identify and resolve potential issues before they effect SLAs
  • Reduced TCO: Minimizes customer TCO by utilizing smart forecasting and storage optimization. This avoids over-provisioning, optimizes investments and generates better ROI for customers.

 

The initial release of Veritas Predictive Insights is an end to end SaaS application hosted in cloud that helps Veritas Support Engineers to proactively identify issues with Veritas appliances, in our later releases these capabilities will be rolled out to Veritas Customers to manage appliances proactively with the eventual goal to build autonomous storage and backup appliances that can self-heal.

 

Picture 2 Predictive Insights.pngVeritas Predictive Insights at its core is a Machine Learning platform that utilizes historical data sets such as:

  • Telemetry data
  • Inventory data
  • Case Data
  • Evidences such as logs collected over years

 

Veritas Predictive Insights utilizes machine learning models to compute a simple health score for the appliances called System Reliability Score (SRS), which is easily understood by the end users. System Reliability Score (SRS) is an additive machine learning model that aggregates inputs from various different machine learning models that predicts using different data sets.

 

In addition to providing a SRS score, end users are provided a set of recommended actions to act on to improve the system health.

  • Storage Forecasting: Uses capacity data toPicture last.png forecast storage trends of the appliances and provides capacity recommendations
  • Configuration Drift: Uses inventory data to identify any patches, firmware and components upgrades to improve the appliance performance 
  • Events Model: Identifies unresolved events and points to ongoing issues with the appliances
  • Predictive Models: Utilizes telemetry data such as Temperature, Iops, and SMART Data to predict various component failures

 

In addition to the machine learning models, Veritas Predictive Insights provides visualization and smart workflows through a cloud hosted multi-tenant SaaS application which the users can act on these recommended actions and provide alternate recommendation and rate the quality of the recommendations. Ultimately, this will help tune the recommendations for other users. By continuous monitoring of customers’ installations, and real-world input from service personnel, the Veritas AI/ML Engine delivers predictive insights about a customer’s environment, resulting in proactive recommendations and actions to improve their business operations.

For more information visit Veritas Predictive Insights webpage.