Challenges addressed by No Code AI platforms

An AI model building is challenging on three fundamental counts:

  1. Availability of relevant data in good quantity and quality: The less I rant about it, the better.
  2. Need for multiple skills: Building an effective and monetizable AI model is not just the realm of a data scientist alone. It needs data engineering skills and domain knowledge also.
  3. The constant evolution of the ecosystem in terms of new techniques, approaches, methodologies, and tools

There is no easy way out to address the first challenge, at least not so far. So, let us brush that under the carpet for now.

The need for having multiple resources with complementing skills is an area where a no-code AI platform can add tremendous value. The average data scientist spends half of his/her time preparing and cleaning the data needed to build models and the other half fine-tuning the model for optimum performance. No Code AI platforms (such as Subex HyperSense) can step in with automated data engineering and ML programming accelerators that go a long way in alleviating the requirement of having a multi-skilled team.  What’s more, it empowers even Citizen Data Scientists with the ability to build competent AI models without having the need to know any programming language or having any background in data engineering. Platforms like HyperSense provide advanced automated data exploration, data preparation, and multi-source data integration capabilities using simple drag-and-drop interfaces. It combines this ability with a rich visual representation of the results at every step of the process so that one does not need to wait until the end to realize an error that was done in an early step and have to go back and make changes everywhere.

As I briefly touched upon a while back, getting the data ready is one-half of the battle won. The plethora of options on the other half is still perplexing – Is it a bird? Is it a plane? Oh no, it is Superman! Well, in our context – it would be more like – Is it DBSCAN? Is it a Gaussian Mixture? Oh no, it is K-Means! Feature engineering and experimenting with different algorithms to get the most optimum results is a specialized skill. It requires an in-depth understanding of the data set, domain knowledge, and principles of how various algorithms work. Here again, No Code AI platforms like HyperSense come to the table with significant value adds. With capabilities like autonomous feature engineering and…

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