Real-World lessons for Machine Learning in business

Oak tree

Machine learning seems to be getting all the interest and hype these days, and some are even saying that it’s going mainstream. There are even dedicated conferences and summits for ML just like the 2021 AWS Machine Learning Summit. For ML to go mainstream, in my perspective, there are still real-world lessons we’ll need to translate ML into production for businesses, and I was hoping to get some takeaways from this summit. I listed here some parts that made the most impact on me.

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The Data Science Intrapreneuring Manifesto

Intrapreneuring

ewsflash: you don’t work at a hyper-innovative company like Netflix, Google, or Amazon. But if you’re an entrepreneur at heart, can you thrive in a company that resists change? How is innovation in an established company different than in a startup? Gifford Pinchot tackled these questions in his ground-breaking book, Intrapreneuring, in 1985. If you’re a data scientist in a big company today, you’re an intrapreneur — an entrepreneur inside, or intra, a company. So I modernized Pinchot’s principles to create a manifesto for data science intrapreneurs.

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5 reasons why companies need to develop and prioritize their AI Business strategies now!

Man playing chess

In 2018, a study by MIT Sloan Management Review exposed that 58% of companies believed AI will significantly change their business models by 2023. And in 2019, a Forbes article that indicated 73% of top U.S. executives have a goal to increase investment in technology dramatically. Now more than ever, companies recognize that AI is essential to their business growth.

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The Pareto Principle — Spending time and energy effectively as a Data Scientist

The Pareto principle

A small portion of effort causes the majority of payoff. The Pareto Principle states that, for a wide variety of situations, about 80% of the outcome is caused by around 20% of causes. It turns out this is widely applicable, both to how you look at data, and how you think about projects.

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Do companies need a Chief AI-Ethics Officer?

Ethical workflow

The world we live in is becoming more and more data-driven. This is causing companies to make more and more use of AI techniques such as machine learning and deep learning. The task of the Chief AI Ethics Officer (CAIEO) should not be primarily technical. Instead, it should sensitize data scientists, machine learning engineers, and developers to ethical issues. The whole process should be firmly integrated into the respective process models and phases.

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How do you define Business Model Innovation?

In 2020, all competitive companies are in a state of constant business transformation. That’s just a fact : those that do not evolve are made redundant by stronger, more agile competitors. And so, mediocre companies update their products and services. Good companies update their processes, too. Great companies, meanwhile, update their business models.

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How to combine design & business thinking in Product Development

Design Thinking & Business Thinking in Product Development

Long-life living for only getting profit has not much sense. Similarly, products created for “making money” become part of the mass-consumerist machine. Nothing special. They are quickly forgotten. They are, and they are not. They live for a while and bring very temporary value. 

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