Magic Quadrant for Data Science and Machine Learning Platforms
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling that support the independent use by, and collaboration between, data scientists and their business and IT counterparts through all stages of the data science life cycle. These stages include business understanding, data access and preparation, experimentation and model creation, and sharing of insights. They also support machine learning engineering workflows including creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances and/or as a fully managed cloud offering.
Vendors must, among other requirements:
A: This research covers the Data Science and Machine Learning (DSML) platform market, evaluating 20 vendors across their ability to execute and completeness of vision. It assesses platforms that support the full data science life cycle including business understanding, data access and preparation, experimentation and model creation, sharing of insights, and machine learning engineering workflows. The research particularly focuses on how vendors are incorporating generative AI capabilities, supporting multiple user personas (expert data scientists, business users, and fusion teams), and enabling enterprise-scale deployment of ML and AI models.
A: This research should be used by data and analytics leaders who are making critical decisions about selecting a DSML platform vendor. It is valuable for organizations evaluating platforms to support expert data science teams, line-of-business teams using low-code development, or multidisciplinary fusion teams. The research helps buyers understand vendor positioning, strengths, and cautions across key evaluation criteria including product capabilities, viability, market understanding, innovation (especially in GenAI), and ability to serve enterprise needs across different deployment scenarios and use cases.
A: Vendors must provide capabilities to import data from various sources (databases, data warehouses, file stores on-premises and cloud), build and evaluate models using a comprehensive library of data science and machine learning techniques, and deploy, host and serve models within the platform for use in services and applications.
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A: Ability to Execute focuses on the vendor's current capabilities to deliver features and functionality, financial viability, sales effectiveness, market responsiveness, marketing execution, customer experience quality, and operational efficiency. It assesses how well vendors can execute on their offerings today. Completeness of Vision evaluates the vendor's understanding of market direction and future needs, their strategic positioning with respect to GenAI and DSML market, marketing and sales strategies, product portfolio depth and breadth, business model soundness, vertical focus, innovation commitment, and geographic strategy. It assesses the vendor's vision for where the market is heading and their ability to shape or lead that direction.