How Data Science & Machine Learning Platforms Are Reshaping Industries in 2025 and Beyond

 


QKS Group (formerly Quadrant Knowledge Solutions) Predicts a 29% CAGR for the U.S. Data Science & Machine Learning Platforms Market by 2028

This expansion is fueled by the increasing adoption of AI-driven technologies across industries and rising investments in data science capabilities. As businesses seek to harness the power of data for strategic decision-making, demand for advanced platforms is expected to surge.

Key growth drivers include the integration of AI for automation and analytics, advancements in deep learning and natural language processing, and the growing role of machine learning in business operations. Cloud-based platforms are anticipated to lead the market due to their scalability and flexibility, while regulatory developments in data privacy and ethical AI will shape industry trends.

Additionally, the convergence of IoT, big data analytics, and AI is opening new opportunities in sectors such as healthcare, finance, retail, and manufacturing. Overall, technological innovation and the need for data-driven insights will continue to drive market growth.

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Key Questions Addressed in This Study:

·       What is the current and projected competitive landscape of the U.S. Data Science & Machine Learning Platforms market?

·       What will be the key competitive dynamics in the market through 2028?

·       How will vendors position themselves across different customer segments, from SMBs to large enterprises?

·       How will cloud-based and on-premises solutions compare in vendor offerings by 2028?

·       What are the strengths and challenges of vendors operating in this market?

·       How will major industries contribute to market growth, and what competitive factors will shape vendor positioning?

Strategic Market Insights

According to Quadrant Knowledge Solutions, “A data science and machine learning platform is a unified system that combines code-based libraries with low-code/no-code tools. It enables collaboration among data scientists, data engineers, and business analysts throughout the data science lifecycle from business understanding and data preparation to visualization, experimentation, model development, and insight generation.

These platforms also support machine learning engineering tasks, including data pipeline creation, feature engineering, deployment, testing, and predictive analytics. Businesses can choose between local clients, web browsers, or fully managed cloud services based on their operational needs.”

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Vendors covered in this Study:

IBM, Mathworks, DataRobot, Dataiku, H2O.ai, SAS, Databricks, Alteryx, Altair, Iguazio, KNIME, Google, Microsoft, AWS, Cloudera, Samsung SDS, TIBCO Software, Tellius, Alibaba Cloud, dotDATA, Domino

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