SPARK Matrix™_Data Science & Machine Learning (DSML) Platforms

1. QKS Group’s comprehensive study on the Data Science and Machine Learning (DSML) Platforms Market provides a detailed examination of the industry’s short-term and long-term growth opportunities, competitive dynamics, and future trajectory. This research report serves as a strategic resource for technology vendors, business leaders, and end users—enabling them to better understand the evolving DSML landscape, identify emerging trends, and make informed decisions about technology investments and partnerships. The study offers a thorough evaluation of the global market environment, including the key factors driving demand for DSML platforms, major challenges impacting adoption, and the technological innovations shaping the next generation of solutions. With industries across the world embracing data-driven transformation, DSML platforms have become essential enablers of business intelligence, operational efficiency, and innovation. Comprehensive Vendor Evaluation through SPARK Matrix™ At the core of the research is the proprietary SPARK Matrix™ analysis, a unique framework developed by QKS Group to assess and position vendors based on their technology excellence and customer impact. This evaluation provides a visual representation of each vendor’s market standing, offering clear insights into their strengths, weaknesses, and differentiation strategies. The SPARK Matrix™ for the DSML Platforms Market includes detailed analyses and positioning of leading global vendors such as Alibaba Cloud, Altair, Alteryx, Anaconda, AWS, Cloudera, Databricks, Dataiku, DataRobot, Domino Data Lab, dotData, Google, H2O.ai, IBM, Iguazio, KNIME, MathWorks, Microsoft, Samsung SDS, SAS, Tellius, and TIBCO Software. Each of these vendors contributes uniquely to the DSML ecosystem, providing diverse tools for data integration, model building, predictive analytics, and artificial intelligence (AI) automation. The evaluation not only compares their technology offerings but also assesses their scalability, usability, and alignment with emerging trends such as cloud- native development, automation, and AI democratization. Evolving Role of DSML Platforms in the Modern Enterprise According to Akash Dicholkar, Analyst at QKS Group, Data Science and Machine Learning (DSML) platforms are rapidly becoming integral to a broad range of industries, far beyond their traditional applications in statistics or research. Modern DSML platforms empower a wide variety of users—ranging from expert data scientists to non- technical business analysts—by offering both code-based and low-code/no-code environments.

2. This flexibility has significantly expanded the accessibility of AI and machine learning, allowing organizations to harness data insights without requiring extensive programming expertise. As businesses face mounting pressure to make faster, data- backed decisions, the democratization of data science tools has become a key competitive advantage. Moreover, Data Science and Machine Learning (DSML) Platforms platforms now serve as the foundation for enterprise-level automation and intelligent decision-making systems. They enable teams to collect, clean, and analyze data efficiently; build predictive and prescriptive models; and deploy them at scale across various business functions—from marketing and supply chain optimization to risk management and customer experience. The Impact of Generative AI on DSML Capabilities One of the most transformative developments highlighted in QKS Group’s report is the integration of Generative AI (GenAI) into DSML platforms. Generative AI technologies are redefining the landscape of data science by enabling systems to generate synthetic data, simulate complex environments, and enhance model training with greater efficiency. As Akash Dicholkar notes, “The incorporation of emerging technologies such as Generative AI (GenAI) is poised to significantly impact the capabilities of DSML platforms. GenAI’s ability to generate synthetic data, improve anomaly detection, and optimize model performance brings new opportunities for innovation and efficiency.” By leveraging GenAI, organizations can address some of the biggest challenges in machine learning, including data scarcity, model bias, and long training cycles. Synthetic data generation helps supplement real-world datasets while maintaining privacy and compliance. Meanwhile, enhanced anomaly detection algorithms powered by GenAI enable faster identification of irregularities and potential threats, particularly in sectors such as finance, healthcare, and cybersecurity. As DSML platforms continue to evolve with these advanced capabilities, they are expected to offer more robust, adaptive, and intelligent solutions for data analysis, prediction, and automation. Market Dynamics and Growth Drivers The growing demand for DSML platforms is driven by several factors, including: •Explosion of Data: Organizations across industries are generating massive amounts of structured and unstructured data, fueling the need for scalable analytics platforms.

3. •Digital Transformation Initiatives: As enterprises accelerate their digital transformation journeys, DSML platforms have become a cornerstone for automation, AI-driven insights, and process optimization. •Integration of Cloud and Edge Technologies: Cloud-native DSML platforms enable flexible, scalable deployments, while edge AI expands analytical capabilities to real-time decision-making environments. •Rising Focus on Low-Code AI Development: Businesses increasingly prefer platforms that empower citizen data scientists, reducing reliance on specialized technical skills. •AI Governance and Ethical AI: With growing concerns around model transparency and fairness, DSML vendors are enhancing governance, monitoring, and explainability features to build trust in AI outcomes. These trends collectively point toward a robust growth trajectory for the DSML market over the next several years. Enterprises that successfully leverage these platforms can expect not only operational improvements but also new sources of innovation and revenue. Future Outlook: Towards a Smarter, More Accessible DSML Ecosystem Looking ahead, the DSML landscape is expected to undergo a significant transformation as emerging technologies such as GenAI, AutoML, MLOps, and multimodal learning continue to mature. Platforms will increasingly focus on end-to-end automation—from data preparation and model development to deployment and lifecycle management— creating a seamless and intelligent analytics environment. Additionally, greater emphasis will be placed on collaboration and interoperability, allowing diverse teams to work together across data, engineering, and business domains. Open-source frameworks and API-driven architectures will further accelerate innovation and adoption. As Akash Dicholkar emphasizes, this evolution reflects a broader trend toward more versatile and accessible DSML tools that cater to the growing demand for data-driven insights and strategic decision-making. By integrating advanced AI capabilities and simplifying complex workflows, DSML platforms will continue to empower organizations of all sizes to turn data into competitive advantage. In conclusion, QKS Group’s market research underscores that the Data Science and Machine Learning Platforms market is entering a new era—one defined by accessibility, automation, and intelligence. With innovation accelerating across every

4. layer of the analytics ecosystem, vendors and enterprises alike must adapt to remain competitive. The integration of technologies like Generative AI marks just the beginning of a profound shift toward smarter, more scalable, and user-friendly data science environments that will shape the future of business decision-making worldwide.