Machine Learning & Artificial Intelligence driving innovations in Data Science

According to Research and Market, the global data science platform market is estimated to reach $79.7 billion by 2030, growing at a CAGR of 33.6% from 2021 to 2030. The global data science platform market was valued at $4.7 billion in 2020.

Data science advancements are made possible by the right platforms and technology. Data science platforms that support the development, training, scaling, and deployment of machine learning (ML) models are required as the use of machine learning increases. Machine learning and artificial intelligence are also innovating in data science and data management.

It is anticipated that the development of big data technologies and the significance of gathering and utilizing data for decision-making will propel market expansion during the projected period.

What is a Data Science Platform?

Platforms for data science are essential tools for data scientists. A data science platform is a pre-packaged software program that offers the capabilities necessary for a data science project’s life cycle. It makes model creation, model distribution, and data exploration possible. It provides a large-scale computing infrastructure and makes data preparation and visualization more accessible.

Data science platforms offer a centralized platform that encourages user collaboration. Data science platforms act as a one-stop shop for data modeling since they include the APIs necessary for model creation and testing with little help from outside engineers.

Microsoft Corporation, IBM Corporation, SAS Institute, Inc., SAP SE, RapidMiner, Inc., Dataiku, Alteryx, Inc., FICO, The MathWorks, Inc., and Teradata are some key players to dominate the global data science platform market.

WRITTEN BY

Team Eela

TechEela, the Bedrock of MarTech and Innovation, is a Digital Media Publication Website. We see a lot around us that needs to be told, shared, and experienced, and that is exactly what we offer to you as shots. As we like to say, “Here’s to everything you ever thought you knew. To everything, you never thought you knew”