Gartner recently published ‘Magic Quadrant for data science and machine learning platforms’, which shows that a lot has happened in data science and machine learning space over the last year. Their application has become a priority for multiple organizations. A number of analytic procedures are now available to utilize huge data available to them.
“Data science and machine-learning platforms are increasingly available for a broad spectrum of users. These range from operational workers who make day-to-day decisions based on sophisticated models working behind the scenes, to citizen data scientists who need data science and machine-learning capabilities but have minimal skills in advanced data science, to highly skilled engineers and data scientists who design experiments and deploy models to represent and optimize business decisions.”- Gartner.
The number of vendors in the quadrant remained the same as last year (sixteen), however, some established-vendors lost ground, while some nimble players made big jumps.
In the latest magic quadrant for data science and machine learning platforms, Gartner evaluated software vendors who offer products and services related to development and deployment of data science workloads.
It was surprising to see that tech giants like Google and Amazon couldn’t qualify in the magic quadrant, while Microsoft and SAP failed to make a cut in leaders’ quadrant. The leaders’ quadrant included Alteryx, SAS, KNIME, H2O.ai, and RapidMiner.
H2O was among the visionaries in last year’s magic quadrant, which made jump to leaders’ quadrant, while IBM lost ground from leaders to visionaries’ quadrant. SAS, RapidMiner, and KNIME continued their position in the leaders’ quadrant. 
“This year’s Magic Quadrant includes the term ‘machine learning’ in its title (compare 2017’s ‘Magic Quadrant for Data Science Platforms’). Although data science and machine learning are slightly different, they are usually considered together and often thought to be synonymous,” said Gartner analysts.
Data science and machine learning platforms help data scientists to perform several tasks across data and analytics pipeline, including data access and ingestion, data preparation, feature engineering, advanced modeling, testing, training, etc.
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According to Gartner, the revenue from data science and machine learning platforms grew by 9.3% in 2016, to $2.4 billion, which is more than double the growth of overall analytics and BI market (4.5%).
The growth in data science and machine learning platform market is driven by the demand of end-user organizations to use advanced analytics to improve decision-making across the business.
Source: Gartner
