Excellent and reliable data is becoming increasingly important for companies to stay ahead of their competition. Companies need analyses from their data to gain more and better insights into their business and to make better decisions based on those insights. Data-driven is the magic word here. It turns out that the more companies work data-driven, the more productive and profitable they become.

Big data nowadays contains a wide variety of information because companies are getting more data sources at their disposal. Think of data obtained via cell phones, online shopping systems, social networks, electronic communication, GPS, etc. Especially with the massive amounts of information from social networks, a lot of unstructured data is obtained. Processing and analyzing data is becoming increasingly complex due to the various types of data sources and data types, their composition, format, and rules.

Rapid technological developments as Machine Learning (ML) and automated Machine Learning (AutoML) and the availability and accessibility to ever-increasing computing power make it increasingly easy to quickly analyze large amounts of data and make predictions based on them.

Within the organization, the Data Scientist’s job is to make sense of the enormous amount of structured and unstructured data and find the answers to the essential business questions. The Data Scientist is, therefore, a vital link for enabling fact-based decision-making within the business process. The job description of a Data Scientist can differ from job to job and from company to company. Some are mostly involved in Business Intelligence (BI) and descriptive analytics. Others are more focused on predictive analytics, using ML to create predictive models. What impact has advanced techniques such as Automated Machine Learning on the Data Scientist’s role within the organization? Should they be concerned about their position within the company? Before we formulate an answer to this, first, a brief explanation of the concepts of Machine Learning and AutoML.

What exactly is Machine Learning?

Machine Learning is mainly about recognizing patterns in data. By identifying patterns, it is possible, for example, to predict consumer behaviour, use less energy, predict the weather, or even stock market prices. With the help of Machine Learning, you can build models by using all the data you have collected from the past to predict the future. Machine Learning success lies mainly in combining large amounts of available data, smart algorithms, and fast computers.

This combination is also changing rapidly within Machine Learning. With the onset of big data, people first started with descriptive analytics. Now we see a shift to predictive analytics. Descriptive analytics looks back at what has already taken place and extracts insights from it—mostly aimed at analysts and managers to create a more in-depth understanding. Predictive analytics uses data from the past to look to the future and try to predict it. It is much more challenging to determine what will happen than to analyze what has already happened. This is where the significant advantages of Machine Learning and AutoML come into play. With both techniques, extensive and complex structured and unstructured data can be reviewed and analyzed quickly. The patterns ML finds in your data are used to make the predictions. With the outcomes, realistic expectations and forecasts can be made: a significant difference from a more classic engineered model where an expert tries to understand the world first and then designs a model to make the prediction.

What is automated Machine Learning or AutoML?

Automatic Machine Learning, also called automated ML or AutoML, automates the time-consuming, recurring tasks of developing Machine Learning models. The core technology used in AutoML is hyperparameters search, used for pre-processing elements/models type selection, and for optimizing their hyperparameters. Modern AutoML systems also use their experience to improve their performance. AutoML uses ML to do ML, thus automating part of the job of a Data Scientist.

AutoML generates ML solutions for the Data Scientist without doing endless searches on data preparation, data cleansing, model selection, model hyperparameters, ensemble generation parameters, and model compression parameters. On top of that, AutoML systems assist the Data Scientist in data visualization, model comprehensibility, and model deployment.

Why AutoML has the future

One important driving factor of AutoML’s success is that computation power is continuously becoming more affordable and more available. This trend will continue for the foreseeable future, making AutoML more and more potent.

As AutoML platforms’ functionalities improve, knowledge of building, selecting, and training, ML will become less crucial. To the point that it allows anyone with data to start creating their own models. As a comparison, 20 years ago, building a website required significant knowledge of HTML and such. Nowadays, thanks to tooling like Dreamweaver, WordPress, Squarespace, and more, anyone with an idea can make a website. AutoML can create models that are compatible with what an average to above-average Data Scientist can create and, in some cases, even better. Thanks to AutoML, soon anyone with data and an idea can build a model by themselves. This leads to new concepts such as ‘citizen Data Scientist’ and ‘democratizing data,’ and more roles within an organization that can start using ML. For example, a business analyst can create a Proof of Concept to show the customer how ML can change their working way, speeding up the design process. Alternatively, it also allows startups to create their prototype to show to investors quickly. In this way, it will enable greater creativity and opportunity.

Does AutoML make Data Scientists redundant?

Until now, the ratio of engineered models to Machine Learning models was 70/30. However, because the data volume is growing exponentially and Machine Learning and AutoML have made their entrance, the roles are now reversed, and the ratio changes more to 30/70. Machines can do a lot more work in a shorter time. This seems to be a worrying evolution, but it doesn’t have to be. After all, Data Scientists will continue to be needed to gain insights and translate these into the market’s needs. It is only the real-time execution part that will be lacking. Why would a human waste so much more time than a machine that can do this so much faster? Data Scientists can invest the time this frees up much better and more efficiently in obtaining better and more reliable insights.
Besides, what Data Scientists still add to the table is checking if the data is of sufficient quality, does it contain biases, does it properly represent the real world? And, of course, continue to do analysis.

The new role of the Data Scientist

When anyone can create models based on data, Data Scientists now have time to take on a more strategic part within the organization:

  • Looking more at the bigger picture and a higher level determining where a company is going
  • Ensure that the data is reliable and representative
  • Investigate which information is needed and which metrics are needed to perform tasks best: Improve data quality, better suited to the intended purpose
  • Consider which additional data may improve the models
  • Feature engineering

AutoML cannot replace the Data Scientist domain expertise and task definition but avoids the technical work associated with model development. When dealing with Automated Machine Learning platforms, business analysts, and Data Scientists stay focused on the business problem instead of getting lost in the process and workflow.

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