Data scientists need the ability to explain their models to executives and stakeholders, so they can understand the value and accuracy of their findings.

The ability to interpret a generated model is crucial to ensure compliance with company policies, industry standards, and government regulations.

Here’s an interesting write up on Model Interpretability in Azure Machine Learning Services.

During the training phase of the machine learning model development cycle. Model designers and evaluators can use interpretability output of a model to verify hypotheses and build trust with stakeholders. They also use the insights into the model for debugging, validating model behavior matches their objectives, and to check for bias or insignificant features.

In this episode of the AI Show, explore updates to the Azure Machine learning service model registry to provide more insights about your model.

Also, learn how you can deploy your models easily without going through the effort of creating additional driver and configuration files.

Learn More:

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TensorFlow 2.0 is all about ease of use, and there has never been a better time to get started.

In this talk, learn about model-building styles for beginners and experts, including the Sequential, Functional, and Subclassing APIs.

We will share complete, end-to-end code examples in each style, covering topics from “Hello World” all the way up to advanced examples. At the end, we will point you to educational resources you can use to learn more.

Presented by: Josh Gordon

View the website → https://goo.gle/36smBfW

Wall Street Journal explores the future of satellite internet.

The most reliable streaming providers have typically used cable to deliver content. But that’s all changing with the launch of new and better satellites that could one day give us 5G, low latency data. The Wall Street Journal speaks with the chief of the International Bureau at the FCC to discover how those changes are happening almost overnight.