The Relevance of AI Frameworks in Advanced Data Analytics
Developers of Artificial Intelligence systems (aka AI engineers) rarely code an AI system from scratch. There is no doubt that they could, but in practice it is much more efficient to build an AI system (or in a data analytics context, an AI model), using an AI framework. The latter are specialized tools for building, testing, and deploying AI systems, without having to write lots of code.
AI frameworks include platforms like MXNet, Keras, and TensorFlow. These are accompanied by packages in one or more programming languages. These packages act as proxies for the frameworks, enabling the AI developer to focus on the application layer of the system, as well as its design-related elements, instead of the low-level code that implements the AI system’s functionality. Also, these packages make the architectural implementation of it easy, while the training and testing (evaluation) processes become a walk in the park. Finally, the visuals they provide make the whole pipeline easier to understand and optimize.
Naturally, AI frameworks require some learning, particularly when it comes to the various functions and data structures they have. Fortunately, there are various tutorials as well as specialized books on them, so many data science engineers train themselves in these (e.g. through a course). Also, as more and more companies require AI developers in their data analytics teams, many of these AI specialists quickly gain sufficient hands-on knowledge of these systems and become efficient in creating and refining them.
Nowadays, professionals in advanced data analytics are generally adept in one or more AI frameworks these days. Developers like that are also versatile and quick to learn new technologies, particularly if they are linked to the programming languages they know. Rockstars.ai has access to various such quality people, who are not only competent in Artificial Intelligence tech, but also passionate about it. Feel free to reach out to us to discuss your AI or Data Science recruitment related needs and work out a spec for your future employees.