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Course taker’s view: Enterprise Machine Learning in a Nutshell

Hello everyone!

If there is one course you have to take on openSAP, this is Enterprise Machine Learning in a Nutshell, period. It will help you get an overview of what machine learning is and how it can support business. Claire Donelon presented the module two and a half years ago (oh) and it stills stays relevant. I am very tempted to give you details on its contents here as some kind of lesson on machine learning that I would be the first to attend, but I do not want to be unfair to the course providers, who have kindly shared their resources with us.

So, I am going to restrict myself to a few personal thoughts instead:

  • Expertise and sophistication in machine learning do not replace ‘good old’ business analysis skills. You need to be able to see problems, define them clearly and maybe quantify them (describe them using numbers) adequately in order only to begin thinking if machine learning could be beneficial, or, as often said, ‘add value’ to your situation. However, studying a bit of machine learning, particularly use cases and practical examples of basic algorithms (as opposed to theory and heavy scripting), can exercise your analytical mindset and inspire you to find improvements in situations you never thought could change for the better.
  • Machine learning methods are generally imperfect, unable to make 100% correct predictions all the time and under all circumstances. While they do manage to uncover and benefit from patterns in data fast and with limited human intervention, they tend to come with errors and uncertainty as well. Their inaccuracy can be usually contained, so you know what to expect, but certain areas (e.g. criminal convictions) cannot tolerate any risk at all. Be careful.
  • If you are into building and implementing machine learning solutions on your own, start small, start simple and repeat. The same applies to just reading: the short quizzes at the end of each unit in Enterprise Machine Learning in a Nutshell are an easy way to absorb new knowledge. Meanwhile, please do remember that ‘there is life out there’; do not stress and enjoy your days! (notes to me ūüėČ ).

This is it for now. I hope you’ve liked my post and I invite you to share with me your own number-1 openSAP course.

Regards, Thank you for reading!

AL

PS. Clicking on any of the ‘read the original post’ hyperlinks on the main webpage of the course did nothing for me, despite the link path showing some folder structure for answers to questions. Does anyone have an idea of what is going on?

2 Comments
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  • Hi Aspasia Loukeri,

    Thank you for sharing the valuable information

    Interest in machine learning has grown steadily over the years, and many organizations are aware of the potential impact machine learning tools and technologies can have on their business.

    But the reality is we are still in the early phases of adoption, and the¬†majority of companies have yet to deploy machine learning across their operations. In fact, since the introduction of machine learning models at scale during the dot-com boom, it’s taken nearly two decades for ML models to become¬†mainstream.

    To understand more about how machine learning has progressed, O’Reilly recently issued the results of a new survey that explores the state of machine learning adoption in the enterprise. The findings suggest that only 15% of the 11,000 respondents work for companies that have extensive experience using ML in production.

    Machine learning¬†has enormous potential, but in order to reap the benefits, it’s important to put your organization in a position to take advantage of all of it. By hiring for machine learning-specific roles and leadership, implementing success metrics, and building robust model-building checklists, your organization¬†can start to advance on its¬†machine learning journey.

    Thanks & Regards

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  •  

    Hi Gramson

    I don’t have that much of knowledge on Machine Learning in this forum discussion i know few thing in the subject thank for sharing info..

    Machine Learning (ML) models versus building end-to-end Data Science solutions to real enterprise problems.

    Thanks and Regards,

    raybaby