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Starting an AI (or machine learning) project can be a daunting task at any level, and the steps should be different depending on the context. Itâ€™s important to note that sophisticated algorithms are not a requirement for AI and more often than not solutions may be simple. Even the most basic machine learning algorithm can do a decent job for some problems and once a process is set up, more sophisticated iterations are possible.

An alternative is starting with sophisticated algorithmsâ€”as long as thereâ€™s a good understanding of what those algorithms do and itâ€™s â€œeasyâ€ to get them up and running. You donâ€™t want to start your first iteration setting a large number of parameters you donâ€™t understand.

There are some exceptions, and arguably, choices that depend on many factors, including level of expertise, but in general, itâ€™s feasible to start small, build, and iterate quicklyâ€”you want to build an initial solution that demonstrates value. Even if itâ€™s imperfect, setting up a process, and obtaining data, gets you off the ground. Itâ€™s imperative, however, to ask the right questions, focus on the solution, and the needs of who will be using whatever you build, and be resourceful and creative in combining data, models, and open source frameworks. Hereâ€™s how that applies to different players in the tech space:

Produtos

AI na prática

Por Guilherme Pedroso

Produtos

AI na prática

Por Guilherme Pedroso

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