When building teams
Data Science Platform definition
A cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solution, and for incorporating those solutions into business processes, surrounding infrastructure and products.
Artificial intelligence (AI) is hyped:
Hype about AI is at its peak, but AI must be distinguished from data science and ML. Of course, data science is the core discipline for the development of AI, and ML is a core development of AI, but this is not the whole story. ML is about creating and training models; AI is about using those models to infer conclusions under certain conditions. AI is on a different level of aggregation to data science and ML. AI is at the application level.
Data Science and ML models must be combined to work together with other capabilities, such as a UI and workflow management, to constitute an AI Application.
The Report finds variety of audiences to data science and ML platforms (modified):
Citizen data scientists who are accessing (open) data and building data science and ML models. They come from roles such as business analyst, line of business (LOB) analyst, data engineer, and application developer.
LOB data science teams who address initiatives in areas such as digital marketing, risk management, Customer Engagement Management.
Corporate data science teams who have strong executive sponsorship, and can take a cross-functional approach. They do model building and end-to-end process for building and deploying data science and ML models.
Star (Mavericks) Data Scientists. The Python, R, Apache Spark Gurus.
(Gartner Report Magic Quadrant for Data Science and Machine Learning Platforms, published 28 January 2019 ; by Analysts Carlie Idoine, Peter Krensky, Erick Bretheneoux, Alexander Linden).