How Automated Machine Learning Empowers Data Scientists

And Helped Establish the Role of the AI Analyst

Through automation and built-in intelligence, companies like DataRobot make machine learning and data science accessible to a wider audience. Automation enables more users in an enterprise to become involved in developing machine learning and artificial intelligence (AI) applications, helping to meet the ever-increasing demand for data science expertise.   

Jeff Bezos very astutely compared machine learning to software development in his 2017 letter to shareholders:

 

Over the past decades computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques now allow us to do the same for tasks where describing the precise rules is much harder.  

For years, writing software relied on hard-to-use programming languages and development environments. Then companies like Microsoft and Sun created easier-to-use programming languages, WYSIWYG tools, and software frameworks that made software development accessible and affordable for more businesses. As a result, the size of the development teams and the demand for software engineers grew, and in turn, more experienced software engineers moved from being individual contributors to being experts inside much bigger development teams.

The machine learning industry is following a similar trajectory as the software industry decades ago. Through automation and built-in intelligence, companies like DataRobot make machine learning and data science accessible to a wider audience. Automation enables more users in an enterprise to become involved in developing machine learning and artificial intelligence (AI) applications, helping to meet the ever-increasing demand for data science expertise.   

 

Data Scientists and Automation

Today, data scientist is generally considered the best job in America, and this is true for the rest of the world. However, the demand for data scientists outstrips the supply, creating a very real challenge for businesses who are looking to AI and machine learning to remain competitive.

With the rise of automated machine learning solutions like DataRobot, it only seems natural that the data scientist is at risk of being replaced by software. If software like DataRobot’s incorporates the best practices for AI and machine learning, and empowers more users across the organization to create AI applications, then the logical conclusion would be that the data scientist’s days are numbered.

 

At the end of the day, DataRobot allows data scientists to be more productive, and also serve as a much-needed expert resource for the new role of “AI Analyst.”

We see automation as a boon for data scientists. Instead of dealing with the score of repetitive tasks associated with developing machine learning models - feature engineering, model testing and revisions, and trying to explain model functionality to management - DataRobot automates these tasks while ensuring that data science best practices are observed. This level of automation and accuracy allows enterprises to very quickly develop highly-accurate models for 80% of their projects with data scientists providing some oversight, which gives the data scientists more time to focus on complex challenges.

At the end of the day, DataRobot allows data scientists to be more productive, and also serve as a much-needed expert resource for the new role of “AI Analyst.”

 

The Rise of the AI Analyst

This same automation has created a more democratized and accessible data science environment, ushering in the era of the “Citizen Data Scientist.” With DataRobot, users without knowledge of machine learning algorithms or R and Python coding readily develop machine learning models by simply uploading a dataset, choosing what they are trying to predict, and hitting the Start button.

One segment of users have become especially important to the new world order of AI and machine learning. Business analysts and data analysts are inherently skilled for automated machine learning. With an in-depth knowledge of a company’s data and their business, these analysts can use DataRobot very effectively, attacking a vast majority of a company’s machine learning and AI challenges and allowing the data scientists to focus on the projects that require advanced skills. 

The result: enterprises of all sizes can now build an extremely effective AI team where data scientists, AI analysts, and software engineers can work to quickly and effectively deliver and deploy AI and machine learning applications faster than ever before.

 

Conclusion

Just as experienced software engineers moved from individual contributors to experts, highly technical and experienced data scientists will transform from isolated individual contributors to become key experts in data science-driven organizations. In this case, automation will drive more job opportunities for new and existing data scientists while giving them the freedom to address more interesting and strategic undertakings. 

And, the majority of AI and machine learning projects will be handled by non-data scientists using tools like DataRobot that allow business analysts and data analysts to transform into AI analysts, delivering more value for their companies and advancing their own careers. 

In closing, data scientists should view automated machine learning as an opportunity to create more value for their businesses and their careers, not as a threat to their status.  

 

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About the Author:

Dennis Oleksyuk is the Director of Engineering at DataRobot Labs & AI Services. Dennis is an experienced engineer with a deep understanding of both technology and business and how they interact to produce positive results. Before joining DataRobot, Dennis worked as as Backend Team Lead, Software Architect, and Senior Software Engineer.

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