Machine Learning model monitoring that is
Reliable. Scalable. Instant.
 

 
The quality and flexibility you expect from a custom-built platform, for a fraction of the cost.
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 Model monitoring is a crucial stage of the Machine Learning Operations (MLOps) lifecycle. It allows Machine Learning teams to detect and fix model issues quickly before they impact the end users. Unfortunately, model monitoring is often overlooked as companies race to get their Machine Learning-enabled products to market.

 

Instrument your production models with our monitoring platform, so you can focus on building your next great idea. 

Monitoring as a Service: 
  • Free up to 25% of your ML team’s time from maintenance work

  • Automatically collect failure data while your models are failing - don’t wait for a post-mortem

  • Streamline routine health-checking and retraining on production models

  • Receive notifications at the earliest sign of model failure, before your business is negatively impacted

  • Monitor your models 24/7 without requiring human supervision 

  • Improve overall model performance, meaning your customers get a higher quality product

We Partner with:

ML Start-ups

We help AI/ML start-ups establish their MLOps toolchain. With years of ML consulting under our belt we provide guidance on which tools are needed and when. 

Companies Scaling their ML 

Many companies monitor their ML models manually or support their own monitoring infrastructure. We help scale these processes through automation.

Other MLOps Tools

We are striving for a seamless MLOps experience. To achieve this we are actively seeking integrations with complimentary MLOps platforms. 

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“Revela was a committed, flexible, and dependable technology partner. They listened well, did a great job of keeping us up to date, and I look forward to working with them again.”

Hunter Macdonald, CEO at Tutela Technologies