Operational Excellence Drives Trust in AI
When designing a trustworthy model, it’s vital that you follow best practices related to the operation of the system. When and how should a machine learning model be used? How should you interpret any individual prediction? What is necessary to ensure the system is compliant and secure? How can you ensure that your model doesn’t degrade over time as the data changes? Answering these questions is integral to getting the intended value out of an AI system and safeguarding your enterprise and data.
Trust Dimensions within Performance
Depending on your industry, aligning model performance to regulatory requirements may be an essential step when preparing to put a model into production. Find out how to set up your model for successful review.
Sensitive information for your enterprise, such as revenue numbers, employee performance, salary, personal data, or sales leads may be part of your training data. Find out how to ensure your data and model stay secure.
Not all model predictions are made with the same level of confidence. A trustworthy AI system knows when to be humble. Find out what it means for a prediction to be uncertain, and what you can do about it.
Governance & Monitoring
The best designed model, with poor governance, may still result in undesired and unintended behavior. Find out how to build in good governance and monitoring, to ensure your AI system delivers the value you need in production.
Data Science Fails: Building AI You Can Trust
This ebook outlines eight important lessons that organizations must understand to follow data science best practices and ensure that AI is being implemented successfully.Download the White Paper