As the market continues to evolve, so too must the skills and knowledge of today’s data scientists. Modern “code-free” solutions are leveraging certain platforms to replace data science work of the past. For example, Azure Machine Learning is a codeless platform, and while code is optional, it is not necessary, which makes it more accessible to the general public. Other tools such as Dataiku and Amazon Machine Learning are wizard-based. These can all prove to be very important because of time to market and ease of market entry. A person who is not well-versed in the various coding languages that are required for this level of work can leverage these toolsets and get a working model deployed much faster.
While understanding the mathematical and statistical methods underlying today’s models is still important, as well as a cursory knowledge of the language (Python/SAS/R), it has possibly become more important to learn and understand modern data science platforms. They’re usable by any and all business users. The only time you would not use something codeless is when a very specific and customizable solution is necessary. Productionizing AI and ML solutions is a key differentiator between practitioners that are learning the space compared to those that are leaders in the space. To get a step ahead of the competition, consider removing code-heavy solutions and make the transition to those modern platforms. Technology will not stop progressing, so we need to make the necessary movements to keep up.
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