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Predibase exits stealth with a low-code platform for building AI models

TechCrunch

-based companies, 44% said that they’ve not hired enough, were too siloed off to be effective and haven’t been given clear roles. Or they can choose to use a blackbox off-the-shelf ‘AutoML’ solution that simplifies their problem at the expense of flexibility and control.”

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What you need to know about product management for AI

O'Reilly Media - Ideas

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door.

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How to Launch a Successful Product: Timing, Roles, and Product Launch Checklist 

Altexsoft

Vendors spend months or years designing and building an app, but one mistake during the product release can put the whole project at risk of failure. Or a developer failed to test the app with real users to verify usage scenarios, hoping his idea will take off by itself. Why did you favor this tool over the thousands of similar ones?

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AI Product Management After Deployment

O'Reilly Media - Ideas

The field of AI product management continues to gain momentum. As the AI product management role advances in maturity, more and more information and advice has become available. One area that has received less attention is the role of an AI product manager after the product is deployed.

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The Art of Agile Development, Second Edition

James Shore

Every week, we look at software engineering topics, and this week, I have a special show for you. For this week’s Tuesday Lunch & Learn livestream , I have a special show for you: the new edition of my book, The Art of Agile Development, Second Edition. The Art of Agile Development came out in 2007. and now it’s time for an update.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning - AI

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.