Surge In Innovation in Data Science and ML Platforms, Says Gartner
We are in the heady days of data science and machine learning, say analysts from Gartner as they released the analyst firm’s latest Magic Quadrant for Data Science and Machine Learning (DSML) just last month.
Gartner’s Magic Quadrant for DSML is targeted at the needs of expert data scientists, citizen data scientists, supporting roles of data science, line-of-business data science teams, and corporate data science teams.
DSML platforms
Only products that meet the DSML platform definition of Gartner made it to the list, which Gartner defines as a tool “to source data, build models and operationalize machine learning”.
Gartner took pains to explain that organizations will need to individually assess their data science requirements and that a vendor in the “Leaders” quadrant might not necessarily be the best choice.
Gartner identified 13 noteworthy capabilities to consider in any given DSML platform. This includes capabilities such as data ingestion, data preparation, data exploration, feature engineering, model creation and training, model testing, deployment, monitoring, maintenance, data and model governance, explainable artificial intelligence (XAI), business value tracking, and collaboration.
According to Gartner, key areas where vendors are seeking to differentiate themselves would be the user interface, augmented data science and machine learning, MLOps, performance and scalability, hybrid cloud or multi-cloud support, and support for cutting-edge use cases and data science techniques.
Cloud players
The latest 2021 report is made up of both established and recognized providers as well as startups that address niche areas. It also saw the likes of Alibaba Cloud, Amazon Web Services (AWS), Cloudera, and Samsung SDS added due to adjustments in its evaluation and inclusion criteria.
In the meantime, the three largest public cloud giants: AWS, Google, and Microsoft, could be found angling for the top spot in the Visionaries quadrant.
“Google touts thought leadership in ML research and responsible AI and has recently made a major effort to reorganize its software release schedule,” says a report on Solutions Review. Microsoft has the highest score for its ability to execute and offer diverse capabilities suited for citizens and expert users alike, while AWS features strong MLOps functionality.
“There remains a glut of compelling innovations and visionary roadmaps,” Gartner analysts wrote, noting that innovation remains key to survival and relevance for vendors in this space.
Despite that, Gartner says there is room for smaller players to find success, with some having achieved what Gartner calls “hypergrowth”.
Image credit: iStockphoto/scyther5
