As the global data quantity already follows an exponential trend, machine learning has become present in every application, creating a great demand for general know-how, be it data scientists or computer scientists with related knowledge. Currently, the demand for work to be done surpasses the offer of such professionals, thus automatic solutions have to be found.
The classical machine learning process involves a few default steps that have become default nowadays, namely:
- data engineering
- model selection
- hyperparameter tuning
- the actual model training
Due to the highly repetitive nature of trial and error of these tasks, automation can play a big role in optimizing time spent on them. Automated Machine Learning comes to help the process by adding different optimization techniques that determine data scientists be more productive and achieve similar or better results in a shorter period of time.
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