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Should You Use A No-Code AI Platform? Limits and Opportunities
Lessons learned after implementing a no-code AI solution
While a majority of AI projects still don’t reach production, the interest for no-code AI platforms keeps rising. Indeed, a growing number of startups and large tech firms now propose “easy-to-use” ML platforms.
The idea of being able to build and use a solution based on Machine Learning without being a data scientist is something very interesting for both small and large companies who could empower their employees while dedicating more resources to complex ML projects.
In this article, I will share what have I learned after having implemented one of these no-code AI solutions and analyzed several startups related to this industry. As an AI consultant, my goal was to determine if these solutions could help us increase the chance of having more projects transitioning from proof of concepts (PoCs) to scalable, relevant, and efficient deployed AI solutions.
Why using no-code AI platforms
From an operational perspective, we develop several AI projects during the year for several departments. Most of them only remain PoCs due to a lack of data, investments, leadership, or simply due to the current maturity of Machine Learning.