Source: morningpicker.com
The companies are trying to utilize most of the facilities that the Data Science programs felicitate for them. Thus, they all are adopting Automated Machine Learning broadly. This has to lead to the scientists questioning the organization about their values and about what the Automated Machine learning offers that they cannot. Well, to answer this, we’ll have to go deep and understand automated machine learning and the way it fits into the whole life cycle of Data Science.
Automated Machine Learning can be termed as an umbrella for platforms and tools that automate the steps involved in the selection of the right model and optimization of its hyperparameters for the generation of the best possible model under the provided set of data that are available.
There are two different libraries for this. One being the auto-learn, and the second one is auto-WEKA. Both of these provide Automated Machine Learning capabilities. Moreover, space also has cloud platforms that give an entire ecosystem for automated machine learning. This includes Azure Machine Learning, the Google cloud platform, Amazon Machine Learning, and IBM Watson. Well, these cloud providers are the different categories of machine learning as a service.
The major aim of automated machine learning is to decrease the number of experimentation, trials, and even errors. It goes around a large number of models and the hyperparameters that are required for those models. This helps to detect the best model available for the data that is used. Thus, it takes a lot of time for scientists to do this whereas automated machine learning can perform this more efficiently and faster. It consumes lesser time for experimentation as compared to when the scientists perform the same.