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 and AI 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 machine learning 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.