Gartner predicts that 80 per cent of data scientists will have deep learning in their toolkits by 2018.
Further, the analysts forecast that by 2019 deep learning will be a critical driver for best-in-class performance for demand, fraud and failure predictions.
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“Deep learning is here to stay and expands ML by allowing intermediate representations of the data,” said Alexander Linden, research vice president at Gartner.
“It ultimately solves complex, data-rich business problems. Deep learning can, for example, give promising results when interpreting medical images in order to diagnose cancer early. It can also help improve the sight of visually impaired people, control self-driving vehicles, or recognise and understand a specific person’s speech.”
Today, most common use cases of ML through deep learning are in image, text and audio processing — but increasingly also in predicting demand, determining deficiencies around service and product quality, detecting new types of fraud, streaming analytics on data in motion, and providing predictive or even prescriptive maintenance.
Gartner argues ML and AI initiatives require more than just data and algorithms to be successful. They need a blend of skills, infrastructure and business buy-in.
Machine Learning Talent
According to Gartner, most organisations currently lack the data science skills for simple ML solutions, let alone deep learning.
“In this situation, IT leaders will be seeking specialists, called data scientists,” said Linden. “Data scientists can extract a wide range of knowledge from data, can see an overview of the end-to-end process, and can solve data science problems.”
“If one of your teams possesses a good understanding of data, has business domain expertise and can interpret outputs, it is ready to start ML experiments,” said Mr. Linden.
“Even if your team lacks experience with algorithms, it can start with packaged applications or APIs.”
Identifying business value from ML
Linden recommends finding the right problem to solve before embarking on ML.
“It is a good idea to start ML by using the same data you use in your popular reports, such as orders by a region. Then you can apply ML to make forward-looking predictions, for example a forecast for the same orders by a region for the next month. This way it extends on the after-the-fact reports to show business stakeholders the art of the possible with ML.”
Nevertheless, ML has limitations. “An ML system can make the best possible decision if it has enough data to learn from — such as millions of priced items and their availability — but it cannot judge whether any of the resulting decisions are OK ethically,” added Linden.