Every organization that runs ML models in production has realized the importance of monitoring for model and data health. Without proactive monitoring, model failure can have devastating effects on a model's ROI, customer trust, and company revenue.

In order to be successful, data scientists and ML engineers must be able to detect, root cause, and resolve ML model failures such as decay in model performance caused by data drift or low quality predictions due to data quality issues. 

The team at WhyLabs summarized key factors to consider when deciding whether to build or buy an ML monitoring solution based on hundreds of conversations with customers, open source partners, and industry-leading ML teams who built in-house monitoring solutions. This guide will help answer key questions about the ML monitoring needs of your organization: 

• What is needed to solve my team’s unique problem? A complete solution must support features and integrations, deployment models, and security requirements. 

• What is the total cost of ownership to my team? Estimating labor cost, sticker price, support and infrastructure cost for scenarios across build vs. buy spectrum. 

• What resources are required to get the solution in place? Estimating headcount needs for building, maintaining, and supporting the solution across build vs. buy spectrum. 

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*This post was written by the WhyLabs Team. We thank WhyLabs for their ongoing support of TheSequence.