Published: 13 March 2021
The functions and features of data science and machine learning platforms are evolving quickly to keep pace with a highly innovative space. This research helps data and analytics leaders to evaluate 20 of these platforms across 15 critical capabilities.
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Business and data exploration are an integral part of many platforms, offering a common ground for the vital collaboration between (citizen) data scientists and domain experts.
Citizen data science is now supported by a majority of vendors, bringing the power of machine learning within reach of nonexperts.
Expert model development often requires cutting-edge (open-source) techniques, for which many vendor platforms offer first-class support.
Operationalization is more critical than ever as organizations seek to scale their data science and machine learning, demanding tangible business value from projects.
Data and analytics leaders tasked with incorporating data science and machine learning into their analytics strategies
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Strategic Planning Assumptions
- Alibaba Cloud
- Amazon Web Services
- Samsung SDS
- TIBCO Software
- Data Access
- Data Preparation
- Data Exploration and Visualization
- User Interface
- Machine Learning
- Other Advanced Analytics
- Flexibility and Openness
- Performance and Scalability
- Platform and Project Management
- Model Management
- Explainable AI (XAI)
- Precanned Solutions
- Business and Data Exploration
- Citizen Data Science
- Expert Model Development
Gartner Recommended Reading
Critical Capabilities Methodology