AUTO ML empowering DATA SCIENTISTS

The amount of data generated of each individual and company worldwide are hiking over the years. That’s why the United States considers the Data Scientist to be the number one profession for four consecutive years. On top of that, Auto Machine Learning is empowering Data Scientists more. Half of the work of Data Scientists is done by this advanced technology, so that they can get involved in more productive assignments.



Data Science is all about converting raw data into information and getting insights and understandings about the data. While data science does this job, it depends on other traits such as machine learning, artificial intelligence, statistics, deep learning and also few others. In fact, data science itself is a collaboration of various categories of technologies. 80% of the data science jobs are all about data preparation. This data preparation is about making models with data which would predict a precise outcome of information. The model we refer to is a large table of organized data from which data insights can be taken? Machine Learning comes into place while creating the models for data science projects. Auto ML software such as DataRobot and H2O automate the repetitive work of creating models. We have to feed data for the software while it grinds through iteration of features, models and parameters. But still it is lacking when it comes to expert prediction and domain expertise, while it can replace the brute and repetitive work. And also the trick in using this Auto ML software is feeding in the right data which is entirely up to human intelligence. Auto ML can replace a large amount of brute force done by data scientists, so that they can indulge in more intelligence performances.


Despite all the advancements in Artificial Intelligence, the demand for Data Scientists has increased more. That’s why Data Scientists skilled with AI and ML are in demand. By 2026, the demand will drive by 27.6%. Data science can play a significant role in almost every sector such as business, e-commerce, banking, space observations, medical sciences, airplane route planning, image recognition etc.


The job done by a Data Scientist is wide and vast but for your understanding let us consider one of the applications of a data scientist. The recommendation algorithms we see in YouTube, Tik-Tok, Instagram and all such apps are the work done by data scientists. In a way, these recommendation algorithms have a pulling effect by offering the right video to the right person. For example; let us say a person watches only videos about gaming, then his homepage will be loaded with videos related to games and stuff. In fact, these recommendations are the ones which increase the watch time of the users and keep the users in the app for a long time. Personalized ads are also one of the applications of the data scientists.


Have you ever noticed that when you search for a product in Google, the next day you find ads related to that product in all other apps? Well, it is also one of the applications of data science and it is not an exploitation of your privacy. You can turn off such personalized ads and tracking of your browsing when you want to. Technically, the main aim of a data scientist is to draw out business focused insight from the data available. It consists of data developing methods like recording, storing and analyzing of data, and extracting all the useful information required to use it to identify business opportunities.


The key process of Machine Learning is to make the learning algorithm to find patterns from the input data called training data. A set of new rules is generated by the learning data algorithm based on references from the data. The same learning algorithm can be used to generate different models by using different training data. For example, teaching the computer to translate languages and predicting the stock market can be done by selection, and feature engineering techniques, along with machine learning techniques and optimized parameter settings. For a machine learning specialist, each of these steps can be time consuming and can be crippling.


CONCLUSIONS

Data science combined with machine learning is a great technology which enhances and advances in all the sectors and creates a lot of wonders.