AI's Impact in 2020: 3 Trends to Watch
The popularity of AI and ML have wide-reaching effects on your enterprise. Here are three important trends driven by AI to look out for next year.
- By Ryohei Fujimaki
- December 2, 2019
[Editor's note: Upside asked executives from around the country to tell us what top three trends they believe data professionals should pay attention to in 2020. Ryohei Fujimaki, Ph.D., founder and CEO of dotData, focused on AI and ML.]
The Rise of AutoML 2.0 Platforms
As the need for additional AI applications grows, businesses will need to invest in technologies that help them accelerate the data science process. However, implementing and optimizing machine learning models is only part of the data science challenge. In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that preceded the selection and optimization of ML models such as feature engineering -- the heart of data science.
This means that organizations will need to look for new, more sophisticated automated machine learning platforms. These "AutoML 2.0" tools will need to provide end-to-end automation, from automatically creating and evaluating thousands of features (AI-based feature engineering) to the operationalization of ML and AI models -- and all the steps in between.
The Shift to Automation Will Intensify Focus on Privacy and Regulations
As AI and ML models become easier to create using advanced "AutoML 2.0" platforms, data scientists and citizen data scientists will begin to scale ML and AI model production in record numbers. This means organizations will need to pay special attention to data collection, maintenance, and privacy oversight to ensure that the creation of new, sophisticated models does not violate privacy laws or cause privacy concerns for consumers.
As a result, in 2020 we will see an emergence of new tools that will enable data scientists to have greater transparency without sacrificing accuracy. This shift to a more "white box" approach to data science will deliver more transparent and accurate models thereby empowering businesses to make data-centric decisions and accelerating their digital transformations.
More Citizen Data Scientists Doing Data Science
Big data will continue to be on the upsurge in 2020 with a growing demand for skilled data scientists and a continued shortage of data science talent -- creating ongoing challenges for businesses implementing AI and ML initiatives. Although AutoML platforms have alleviated some of the pressure on data science teams, they have not resulted in the productivity gains organizations are seeking from their AI and ML initiatives. As such, companies need better solutions to help them leverage their data for business insights.
In 2020, we will see a swift adoption of new, broader, "full-cycle" data science platforms that will significantly simplify tasks that formerly could only be completed by data scientists and boost the productivity of citizen data scientists -- business analysts and other data experts who have domain expertise but are not necessarily skilled data scientists. This continued democratization will lead to new use cases that are closer to the needs of business users and will enable faster time-to-market for AI applications in the enterprise.
About the Author
Dr. Ryohei Fujimaki is the founder and CEO of dotData. In his career, Dr. Fujimaki was the youngest research fellow ever in NEC Corporation’s 119-year history, the title was honored for only six individuals among over 1000 researchers. During his tenure at NEC, Ryohei was heavily involved in developing many cutting-edge data science solutions with NEC’s global business clients, and was instrumental in the successful delivery of several high-profile analytical solutions that are now widely used in industry. You can reach the author via email or LinkedIn.