When a new trend emerges like Data Science Artificial Intelligence

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Automated data cleaning has been extensively researched in recent years. Cleaning big data often takes up a lot of valuable data scientist time. Both startups and large companies like IBM offer automation and tools for data cleaning.

Another significant sub-field of Data Science, known as feature engineering, has undergone significant innovation. Featuretools offers solutions for automated feature engineering. In addition, modern deep learning methods such as convolutional and recurrent neural networks learn their features without the need for manual feature engineering.

Automation is perhaps most visible in the area of ​​machine qatar number data learning. Both Data Robot and H2O have established themselves in this space by offering comprehensive machine learning platforms that make data scientists and data analysts very easy to navigate with data management and model development. AutoML, a method of automatically developing and training models, also flourished during 2019 as these automated models outperform state-of-the-art research. Google, in particular, is investing heavily in Cloud AutoML.

Overall, companies are investing heavily in building and acquiring tools and services to automate Data Science. All to make the process cheaper and easier. At the same time, automation also allows for smaller and less technical organizations to use these tools and services to access Data Science without building their own team.