Visualizations are a powerful tool for communicating results to end-users and stakeholders. Their development and life-cycle management are no less challenging than the underlying processes producing the results they communicate.

Databricks explains how this is helping with the COVID pandemic.

Our team overcomes these challenges by leveraging Vega-Lite to encode visualizations as JSON objects and using the MLflow model registry as a visualization registry. During this presentation, we will walk through the process of creating a multi-layered Vega-Lite visualization using COVID-19 and Geodata, then managing it with the MLFlow model registry.Visualizations are a powerful tool for communicating results to end-users and stakeholders. Their development and life-cycle management are no less challenging than the underlying processes producing the results they communicate. Our team overcomes these challenges by leveraging Vega-Lite to encode visualizations as JSON objects and using the MLflow model registry as a visualization registry. During this presentation, we will walk through the process of creating a multi-layered Vega-Lite visualization using COVID-19 and Geodata, then managing it with the MLFlow model registry.

Azure Cosmos DB shares some optimization tips in this short video.

A well-planned partitioning strategy will optimize database reads and writes, enabling you to achieve great speed at any scale with Azure Cosmos DB. In under two minutes, you’ll gain an understanding of how partitioning works, why it’s critical to performance as your database scales, and how to select the best key for your database. 

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AI  is becoming a fundamental part of protecting an organization against malicious threat actors who themselves are using AI technology to increase the frequency and accuracy of attacks and even avoid detection, writes Stuart Lyons, a cybersecurity expert at PA Consulting.

“What is becoming clear is that engineers and business leaders incorrectly assume that ubiquitous AI platforms used to build models, such as Keras and TensorFlow, have robustness factored in. They often don’t, so AI systems must be hardened during system development by injecting adversarial AI attacks as part of model training and integrating secure coding practices specific to these attacks.”

Join Seth Juarez as he delves into ethical concerns with AI with Josh Lovejoy, who leads Design for Microsoft Ethics & Society within Cloud + AI, and Sarah Bird, who leads Responsible AI for Cognitive Services.

This video explores how to think about Ethical AI and how to ensure the software you build is designed, developed and deployed ethically. Josh and Sarah describe how Ethics & Society works closely with product teams to make human-centered AI / ML technologies that serve people by appreciating the constraints and measuring accuracy and inaccuracy throughout the product development lifecycle.

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