Industry Predictions: AI, Machine Learning, Analytics & Data Science Main Developments in 2018 and Key Trends for 2019

As we continue to bring KDnuggets readers year-end roundups and predictions for 2019, we reach out to a number of influential industry companies for their takes, posing this question:What were the main developments in AI, Machine Learning, Analytics & Data Science in 2018 and what key trends do you expect in 2019? For the industrys take on what happened this year and what will happen next, we have collected insights from Domino Data Lab, dotData, Figure Eight, GoodData, KNIME, MapR, MathWorks, OpenText, ParallelM, Salesforce, Splice Machine, Splunk, and Zoomdata.Key themes singled out by these experts include the changing analytics landscape, how data science will continue to influence business, and the emerging technologies that will be leveraged to do so.Be sure to check out collected opinions we shared last week when we asked a group of experts the related question, “What were the main developments in Data Science and Analytics in 2018 and what key trends do you expect in 2019?”  Josh Poduska is Chief Data Scientist at Domino Data Lab.AI: from hype to business impact in 2019..The honeymoon is officially over for Artificial Intelligence..2019 will be the year that AI will become an organizational reality, rather than experimentation, tinkering, and doubt..Forget Googles AI call center agent..Data sciences biggest impact will be in the spots you dont think about….in less “sexy” parts of the business, like faster servicing customers a technical support calls, optimized inventory, smarter product shelving, reducing wasted time on purchases, and more.The consumers understanding around AI will shift dramatically..We will no longer associate AI will futuristic robots and self-driving cars, but rather productivity tools and predictions to help everyday, menial tasks..  Dr..Ryohei Fujimaki is CEO and founder of dotData, the first company focused on delivering end-to-end data science automation and operationalization for the enterprise.The pressure to achieve greater ROI from AI and ML initiatives will push more business leaders to seek innovative solution..While substantial investments are being made into data science across many industries, the scarcity of data science skills and resources limits the advancement of AI and ML projects within organizations..In addition, one data science team is only able to execute several projects a year given the iterative nature of the process and the manual work that goes into data preparation and feature engineering..In 2019, data science automation platforms will capture much of the mind share.. More details

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