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What Enterprise Software Delivery Can Learn From a Woman, Hackathon, and ML

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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.

· AI Zone ·
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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.

By exploring the massive and intensely interesting data that surrounds the act of building and delivering software, we will begin to see patterns in software development that can impede the speed of delivery. We can understand what team structures and sizes are most effective. We can learn how dependencies between products and requirements increase technical debt. We can learn what ratios of capacity are going to features, defects, and tech debt that are appropriate in different stages of a product’s maturity. We can understand the effects of collaborative development techniques and their effects on employee and team happiness. The list goes on.

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.

artificial intelligence ,machine learning ,deep learning ,hackathon ,software delivery ,ml applications ,women in software

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