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5 Machine Learning Lessons for Product Managers

Mind the Product

Artificial intelligence (AI) is probably the biggest commercial opportunity in today’s economy. We all use AI or machine learning (ML)-driven products almost every day, and the number of these products will be growing exponentially over the next couple of years. What does it mean for us as product managers?

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Generative AI in Your Business Strategy: From Concept to Reality

The Product Coalition

The AI Journey So Far The encouraging news is that most enterprises have already embarked on their artificial intelligence journey over the past decade years. Industries such as high tech, banking, pharmaceuticals and medical products, education and telecommunications, healthcare, and insurance stand to gain immensely.

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Product Management for Financial Services

ProductPlan

With company leadership, product management sets the strategic direction for the product(s). Personal finance, banking, and credit. For instance, the delivery of mobile consumer banking services involves multiple product managers or product management teams. The online banking product manager. Lending and underwriting.

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Product Model, Service Models, and Investor Valuations

Mironov Consulting

 But when I do product due diligence for SaaS-focused PE/VC firms, it's the very first thing I look at.  Let’s “MegaCorp needs a special connector to a home-grown credit reporting system.”  Can we partner with some services-only firms and teach them how to implement our system?  How

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Product Management Tips for Data Science Projects

Mironov Consulting

Data science has traditionally been an analysis-only endeavor: using historical statistics, user interaction trends, or AI machine learning to predict the impact of deterministically coded software changes. Increasing, though, companies are building statistical or AI/Machine Learning features directly into their products.

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BoS USA 2023 – The Sigmacorn Notes

Business of Software Conference

Linear for project management) Decisions made on trend may be conjoined in contextual groups, similar to pillars Segmenting market to reach “best of both worlds” For example: Red Hat (RHEL), mongoDB, elastic, Slack Enterprise vs core technology mass-market consumer product Choices can be taken to extreme levels to distinguish a small company in a competitive (..)

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How bots, messengers and apps will define 2019 with Des Traynor, Paul Adams and Emmet Connolly

Intercom, Inc.

And then you can get smarter with machine learning and stuff. Bots are great at things that are suitable for computer calculations, like when your next bill is due. Or showing you your bank balance. To take Paul’s example, a bot that tells you your bank balance. Take your bank balance example, there.