Of course this question begs for the answer that it is both an art and a science. I view it more like craftsmanship. This article discusses my opinion on the subject.
We need to start by defining what machine learning is, or more precisely, what kind of work it entails. I broke it down into three types of activities, corresponding to different types of machine learning professionals or job titles. Many practitioners spend some amount of time, in various proportions, on any of these activities.
- Level 1: Professionals in this category are end-users of machine learning platforms or software, and typically their coding abilities are limited, and rarely needed. They use the tools as black boxes, and may not even know (or only superficially) the details of the techniques involved. They have the ability to interpret the output of the platforms that they use, to fine-tune parameters, and the ability to compare the performance of various platforms and techniques. Examples include business analysts, or software engineers asked to integrate algorithms developed by data scientists (those in level 3), into production mode.
- Level 2: In this category, I include people using machine learning tools and platform, with a serious understanding as to how they work, and typically interacting with these platforms as builders and developers, mastering some programming languages to interact with these tools in the most efficient way. They typically don't build new, complex algorithms, from scratch. But they know how to use existing ones to address the problems at stake, within the framework of the platforms that they use.
- Level 3: These people may not necessarily know that much about the tools mentioned in the previous levels, as they develop their own algorithms from scratch, typically to solve new problems not properly solved by the above platforms. They master some programming languages and their libraries (usually including Python), and are experts in algorithm design and optimization.
Read the full article here
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Decision-Centric Strategy for Meeting Objectives
3 年I think too much art and creativity in first steps of analytics results in unnecessary wasted effort and risk, as does being overly focused on tools. I think Level 1 would include knowledge of how machine learning can help achieve some clear and fairly standard deliverables for early stages of analysis. I don't really disagree with anything in the article, except to say that the focus on tools and programming skills skips past an important aspect of successful use of machine learning.
Assistant Professor at University of Twente
3 年Very interesting discussion. I guess the point is how can we make it less of an art and more like a an engineering discipline so that we can train people and make them gain the necessary abilities to obtain success in this area? This is of course in everyone's best interest, correct?
CEO of AC SmartData | NED | Creating Real-World AI Solutions | Advocating Responsible AI
3 年Great article Vincent!