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Training and Operationalizing Interpretable Machine Learning Models
AI offers companies the unique opportunity to transform their operations: from AI applications able to predict and schedule equipment’s maintenance, to intelligent R&D applications able to estimate the success of future drugs. However, in order to be able to leverage this opportunity, companies have to learn how to successfully build, train, test, and push hundreds of machine learning models in production, in ways that are robust, explainable, and repeatable.
During her upcoming session “Training and Operationalizing Interpretable Machine Learning Models” at ODSC East, Francesca will introduce some common challenges of machine learning model deployment and she will discuss the following points in order to enable you to tackle some of those challenges:
- How to select the right tools to succeed with model deployment.
- How to use automated machine learning to optimize your machine learning deployment flow.
- How model interpretability toolkits can be used to build machine learning pipelines that are robust, explainable, and repeatable.
Source: www.aka.ms/AzureMLservice
