It’s a good time to be in the market for data science and machine learning (DSML) tool, going by the latest Gartner Magic Quadrant for data science and machine learning platforms.
While the market landscape is extremely fragmented and complex, vendors are working hard to push out new innovations and differentiate their products. Indeed, even the normally reserved Gartner itself described the DSML market as “beyond healthy” and “thrillingly innovative”.
Top DSML platforms
The analyst firm evaluated competing platforms using a variety of metrics for its latest DSML Magic Quadrant, with the primary users defined as data science professionals. This means expert data scientists, citizen data scientists, data engineers and machine learning engineers and specialists.
Platforms are evaluated based on whether they delivered a platform with coherent integration with components that reasonably interoperable in support of an analytics pipeline. Tasks might include data ingestion, data preparation, data exploration, model creation and training, and collaboration, among others.
Making it into the Leaders quadrant calls for commercially viable, platform-agnostic and mature data science platforms. Unsurprisingly, familiar names such as TIBCO Software, SAS and MathWorks made it into this quadrant with their strong and proven track record, though younger firms such as Alteryx, Databricks and Dataiku also made the cut.
The Visionaries quadrant is for new and emerging offerings and is the most crowded segment this time around with seven players sharing the space. This includes Google and Microsoft, though Gartner says both lack a viable on-premises DSML platform – which works against their score and prevented them from being listed in the Leaders quadrant.
Gartner is optimistic about the DSML market: “The DSML market is beyond healthy and thrillingly innovative. The broad mix of vendors offer a granular range of capabilities, with solutions appropriate for most levels of maturity.”
It noted that vendors are adding more capabilities designed not just expert data scientists or data engineers, but in anticipation of a growing “supporting cast” in the data science life cycle.
“Many vendors are now aiming for a sweet spot with their platforms to simultaneously appeal to and delight both expert data scientists and citizen data scientists. Vendors that previously only catered to expert data scientists are adding augmented capabilities and improved interfaces to appeal to citizen data scientists.”
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