Solutions Review Editor-in-Chief Tim King highlights key takeaways from Gartner's Magic Quadrant for Data Science and Machine Learning Platforms and provides his analysis of the new report.
Analyst firm Gartner predicts 2024 Magic Quadrant for Data Science and Machine Learning Platforms. Researchers define a data science and machine learning platform as “an integrated set of code-based libraries and low-code tools that support independent use by, and collaboration between, data scientists and their business and IT personnel throughout all stages of the data science life cycle.”
DSML platforms can be delivered via a desktop app or browser, on supported compute instances, or as fully managed cloud services. More recently, as Gartner puts it, they can also be purpose-built to:It enables a broad range of users to develop and apply comprehensive predictive and prescriptive tools. Analytical “The machine learning functions included are diverse, ranging from classical regression and decision trees to much more complex functions.”
In short, companies use data science and machine learning platforms to reduce the cycle times and silos typically associated with predictive models. Gartner adds:these are collaboration It also enables asset reuse across multiple teams and departments, and workload orchestration for processing large volumes of data. It also provides a consistent and repeatable training and development environment that provides links between data, code and model assets, making data scientists more productive.”
And recent innovations are enabling line-of-business users who are not data scientists to perform similar tasks as trained engineers through low-code and natural language toolsets. Model building, low-code, notebook-based interfaces and support for MLOps are all what Gartner calls “standard features,” with optional features including auto-suggestions, advanced UI toggles, custom SDKs and GenAI integration.
Innovation among vendors in this enterprise software space is racing to enable GenAI success. One interesting point Gartner makes in a broader context is, “However, the trajectory of this market will depend on the dual capabilities of platforms related to GenAI.” Another key factor that the analysts in the report articulate well is that “DSML activity within the enterprise is growing outside of centralized core DSML teams.”
It is true that many of the vendors in this market are partnering with each other to “share” capabilities that bring greater business value to their mutual customers. Gartner states: “AI and analytics leaders need to understand how these platforms complement each other to provide the best means to deliver value, whether that be low-code, code-first, data management or operational-based capabilities.”
How about concluding the report by declaring, “DSML platforms have never been more important as strategic enterprise assets. The surge in demand for AI solutions, including GenAI, is reaching a peak, but assembling the raw materials of data, models, code, and infrastructure into reliable, scalable products has never been more complex.” Thus, investing in a DSML platform is risky.
GenAI has accelerated and expanded the trend towards democratizing data science in the enterprise, and will continue to do so in the future. Today, the biggest challenge for leaders in this space is the management and governance of distributed activity.
Gartner adjusts its Magic Quadrant assessment and inclusion criteria as the software market evolves. In this Magic Quadrant, Gartner evaluates the strengths and weaknesses of the 20 providers that Gartner considers most important in the market and provides readers with a graph (the Magic Quadrant) that plots vendors based on their ability to execute and completeness of vision. The graph is divided into four quadrants: Niche Players, Challengers, Visionaries, and Leaders.
