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The Data Daily

AutoML: Automating and Democratizing Data Science in Organizations

Last updated: 03-20-2021

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AutoML: Automating and Democratizing Data Science in Organizations

Industries have been leveraging AutoML to enhance data processing and data engineering. However, there are discussions of how AutoML will affect the job of data scientists. Let us understand more about this technology and its role in enhancing the data efficiency of a company.

The digitization and automation across organizations demanded the adoption of data science and advanced data analytics to encourage business growth and agility. With this increased pace of transformation, companies started to employ data scientist teams to address the need for developing machine learning models and analytics algorithms.

Data-driven decision-making in organizations has proved to improve productivity and minimize costs in the long run. Due to the highly technical skills required for the job, the supply of data scientists is limited even now, thus making it difficult for organizations to capitalize on data and create machine learning models to analyze them. This is where AutoML comes in.

Automated Machine Learning is a nascent development in the field of artificial intelligence. AutoML automates the end-to-end machine learning requirements in business operations. This technology enables the development and deployment of machine learning models without any time or skill constraints.

The conventional procedure by data scientists takes a good portion of time since it involves data cleaning, data analysis, identifying machine learning models, running them, conducting parameter tuning, designing the algorithms, and deploying them. Integrating this long process into the workflow of organizations can be difficult and time-consuming. Since there is a shorter supply and high demand for data scientists, it becomes tougher to develop a team.

Automated Machine Learning eliminates all these challenges by automating the process and running several machine learning models at the same time. AutoML also aids the process of feature selection, feature extraction, and feature engineering to run algorithms. The amount of data is increasing each day and so is the adoption of big data in organizations. Hence, AutoML is a desirable technology to reduce the time and complexity in the implementation of machine learning models.

Another commendable benefit of employing AutoML is its role in the democratization of data science in organizations. There is a huge skill gap in most companies concerning the high skill demand for data science. Organizations usually find it difficult to address the need for better machine learning models because of the limited access of people to the field of data science. AutoML for organizations eliminates this gap by encouraging ‘citizen data scientists ’ to perform the tasks without any prior expertise.

It enables employees other than people with data scientist qualifications to contribute to the data science ecosystem with minimal assistance from the data science teams. For example, Cloud AutoML by Google enables businesses to build customized machine learning models with limited skills and expertise in the field. AutoML increases the accessibility of data science and data engineering to a larger audience rather than restricting it to a popular group.

If you want a single-word answer then, No-AutoML will not make data scientists disappear. It will ease the burden on the shoulders of these data experts by taking over repetitive tasks that do not need much attention. AutoML will automate some of their tasks and leave them with those that need highly technical skills. Organizations will still need data scientists to define problems, apply domain knowledge on the issue, and generate reasonable and creative models. AutoML can work alongside data scientists to support them and this course will enable the decentralization of data science knowledge.


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