A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to go over and learn from those data. The life-cycle management of machine learning models is more complex than software. Changing assumptions and ever-changing data means the work doesn’t end after deploying machine learning models to production. These best practices keep complex models reliable. InfoWorld brought this important information to our attention in their article, “The importance of monitoring machine learning models.

Once you have trained the model, you can use it to review data that it hasn’t seen before, and make predictions about those data. To ensure artificial intelligence (AI) models are impactful in the real world, machine learning teams should also monitor trends and fluctuations in product and business metrics that AI impacts directly.

Most organizations have little knowledge of how AI systems make the decisions they do, or how the results are applied in various fields. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. “Explainable AI” is used to describe an AI model, its expected impact and potential biases. Why is this important? Because the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

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