HOW AUTOML SIMPLIFIES DATA SCIENCE INTO A MAINSTREAM CAREER?
Successful advancements of technology often raise the question about the future of work and how the next generation and existing workforce will be trained to compete with such fast-growing machines. But most experts believe that such technologies will expand the scope for technical jobs and also make them much more accessible for people without years of training.
It is also believed that data science is going to follow a similar path of easing out work for untrained professionals. For example, if at present you want to be a machine learning engineer, a decent amount of python or other programming language knowledge along with skills to construct neural networks manually would be sufficient. Although some programming packages do come with the feature which makes it easier to make machine learning models, it’s still crucial to understand a variety of underline computer science which usually takes quite a bit of training.
However, now that ML and data science is becoming more ubiquitous, it is turning into being more user-friendly, having no-code tools that would allow someone to perform sophisticated analysis without knowing programming or math. Google AutoML is one such tool using which without any coding or even knowledge of neural networks, users can easily upload a data set and can start to perform pretty impressive prediction algorithms around sentiment analysis or text classification.
For example, the data science learning website Kaggle has an open data set which is freely available for users. Here users describe something good that happened to them on a particular day say, “I went to the gym this morning and did yoga” – this would be automatically categorized under exercise. The Kaggle dataset is a light-hearted, and relatively intuitive way to train ML models. It is the model which predicts that which description goes under categories to identify how someone would have a good day, such as if they were relaxing or going out into nature.
For someone who has no knowledge of coding, to build an ML model that can identify tens of thousands of categories from scratch is not an easy task. But, with AutoML, it wouldn’t take more than a few clicks to run the algorithm and accurately predict new data that it had never seen before.
However, AutoML is not sufficient to be a good data scientist but the software is a good illustration of how, in the near future, complex tasks like machine learning can become quite accessible to the general population without years of training.