Building Powerful AI solutions with Data Machines
We recently announced a product update to build, test, deploy and monitor AI solutions rapidly using Data Machines. Now we will dive into the capabilities of Data Machine in this series of posts, starting with the power of Data Machines. Each Data Machine is an intelligent engine composed of one or more AI models efficiently working with each other to handle inputs and outputs in a coordinated manner to perform a task.
Traditionally, AI has been monolithic in nature and in its implementation. Once a model is built, tested, in order to use it effectively, a solution is built around the model which then makes it a single purpose software solution that can be used for one function alone. For example, this is evident in the way fraud detection AI models and recommendation engines have been employed for a couple of decades now.
AI solutions are starting to look more flexible and scalable in nature but building an AI solution in a truly decoupled architecture requires significant planning and implementation to achieve a truly scalable solution.
With Qualetics Data Machines, we are taking this advancement several steps further by providing a platform with AI models positioned as building blocks to create solutions powered by AI that solve various business problems. With this revolutionary design concept, you can now have an Anomaly Detection and a Recommendation engine working alongside each other without requiring their own dedicated implementation.
A building block interface like this enables you to create complex AI solutions using, in some cases, very simple AI models to achieve an impactful business need. For example, providing the convenience to a user on a website to ask a question in their native language and receive a response while the source material is in English or some other language. This is achieved by building a Data Machine, with Language Detection and Language Translation models.
Similarly in the illustration shown above, a single Data Machine can extract the emotion and summarize the text provided as the output with a single inference call.
In the next post, we will discuss more capabilities of the Data Machines such as Self-Learning and Memory.
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