Model monitoring and its importance in production AI systems

The quality of an organization’s decision-making depends on the quality of its data—and organizations that make efforts to improve data quality see tremendous benefit. One economic impact study from Forrester found that keeping models accurate over time helped increase total profits anywhere from $4.1 million to $15.6 million while reducing model monitoring efforts by 35-50%.

Monitoring machine learning models will enable you to analyze the accuracy of predictions, eliminate prediction errors, and tweak models to ensure the best performance—ultimately helping you make better decisions. 

Read this whitepaper to understand:

  • Why model monitoring is important 
  • Approaches to monitor drift
  • How Fosfor Refract helps optimize model monitoring

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Augmented Intelligence: Six best data analytics practices for moving beyond BI

Read this exclusive Fosfor-sponsored TDWI report to learn how you can use Augmented Intelligence to drive better business decisions at every level in the enterprise

Read more

Benchmarking Snowflake vs Spark to optimize DataOps

Read this whitepaper to understand core differences between Apache Spark and Snowflake, and see how they respectively perform on our 5-dimensional benchmark.

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Demystifying Explainable AI for business decision making

Read this whitepaper to learn how explainable AI frameworks make AI-driven decisions more reliable and trustworthy for business users.

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