What Enterprise Software Delivery Can Learn From a Woman, Hackathon, and ML
Let's look at how Machine Learning can improve how software is delivered at scale.
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What a special experience. An old friend and colleague, Lynn Pausic, one of the co-founders of Expero — a company with extensive experience in Machine Learning applied to complex business and technical problems — asked if I would help judge a “Machine Learning hackathon for women.” How could I say no to that?
Eight teams of women presented highly innovative and varied ideas for Machine Learning that could be applied to do good in the world, help improve and save lives, and even make home-cooking easier!
Other ideas included applying Machine Learning to help fire departments predict what kind of calls they would get based on what type of large-scale disaster/event has occurred; to parse conversations on things like Slack to be able to identify “angry” conversations; and help understand why reviews for a company product were positive or negative.
But putting aside the inspirational aspect of being surrounded by incredibly talented women tackling highly technical, scientific, and fun topics, I realized something else important.
Software delivery and the act of the producing software — the very thing that all of these women were doing all weekend — is sadly behind the times with regards to the very thing that has the potential to dramatically affect change and improve what drives the world economy. Software needs more Machine Learning.
I would love to hear from software professionals out there — how do you think Machine Learning could be applied to improve how software is built and delivered, especially at large enterprises? There is huge potential at all levels, from the practitioner side of things, right up to how a business operates.
In fact, we believe that by focusing on Value Stream Management and the flow of value through the massively complex networks of tools, artifacts, and activities required to deliver software at scale, organizations have the opportunity to drastically affect and improve business outcomes.
My Sunday afternoon was spent being inspired by creative, focused, and data-driven women. And I’m excited to take that inspiration and apply it to what I’ve been dedicated to professionally for many years — how to improve how software is delivered at scale; one Machine Learning application at a time.
Published at DZone with permission of Nicole Bryan, DZone MVB. See the original article here.
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