Difference between revisions of "Best Practices"
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• Cloud-enhanced ML | • Cloud-enhanced ML | ||
• Featured use case demo: hospital readmission prediction (addresses running in InterSystems IRIS of the models trained outside the platform's control) | • Featured use case demo: hospital readmission prediction (addresses running in InterSystems IRIS of the models trained outside the platform's control) | ||
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| + | <b>Conversational AI: Best Practices for Building Bots | ||
| + | </b><br>Conversational digital affordances are fast becoming a norm for users everywhere – from the office to the kitchen; from the car to the living room. We can type, tap or talk to all manner of device, apps, bots and agents to do all manner of things. When designed well, conversational AI experiences are natural, intuitive and efficient. In this session, we’ll provide practical guidance for building great bots. We’ll put the guidance to work using the Bot Builder v4 SDK, bot development tools and [[Microsoft]] Cognitive Services. | ||
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| + | <b>Visualization Best Practices for Explainable AI | ||
| + | </b><br>How do you know if a machine learning model is trustworthy or potentially biased? One of the top trends in analytics is the rise of explainable AI, the practice of presenting transparent views into how algorithms make decisions. In this session, we will dive deeper into understanding and explaining machine learning terms and charts to the business using Tableau. We will walk through real-world examples from healthcare, retail, marketing, banking, and other industries. | ||
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Revision as of 20:09, 9 September 2020
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- Rules of Machine Learning: Best Practices for ML Engineering | Martin Zinkevich - Google
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