Build vs Buy - ML Monitoring

While DevOps monitoring tools are widely available, they lack the ability to solve specific operational challenges in Machine Learning Ops. A purpose-built MLOps monitoring solution is key to making your AI/ML deployments successful.

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Key
Considerations

While DevOps monitoring tools are widely available, they lack the ability to solve specific operational challenges in Machine Learning Ops. A purpose-built MLOps monitoring solution is key to making your AI/ML deployments successful.

ML Models are black-boxes

ML models can be inherently opaque, which makes issue identification time consuming.

ML Models Have Unique Needs

ML models have distinct operational challenges from traditional software systems including data drift, outliers, data integrity, performance and bias.

Open Source Challenges

Popular open source monitoring tools help teams build service level visibility quickly but need significant development to support MLOps for ML’s unique operational challenges.

Talent Constraints

Developing a solution that addresses the needs of ML monitoring requires access to specialized skill sets, like explainability & scalable infrastructure.

Enterprise  Readiness

An enterprise-grade production quality ML monitoring system needs to be fast and reliable as well as scale with the needs of the business.

Total Cost of  Ownership

Systems built in-house have a fixed set of requirements, a high initial cost of development and constant maintenance needs. Costs for a homegrown enterprise grade ML monitoring solution can exceed $500k over 3 years.

Cross  Functional Use

Monitoring ML systems is a coordinated effort across Data Scientists, ML Engineers and DevOps that needs intuitive experiences for ease of workflow adoption and hand-off.

Buying an ML Monitoring Solution

Save time in tracking and diagnosing AI performance issues with quick and actionable alerts.

Enterprise grade

Top ML operational alerts

Fast issue resolution

Seamless workflow integration

Intuitive

Wide model support

Deployment flexibility

ML Monitoring with Fiddler

Save time in tracking and diagnosing AI performance issues with quick and actionable alerts.

Best ML operational alert coverage

Powered by industry-leading Explainable AI

Pluggable into any ML Platform

Top ML model framework support

Patented intuitive experiences

Cloud or on-premise deployment

Built for scale, speed and reliability

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