SPARK Matrix™: Data Science & Machine Learning Platforms, Q1 2025 — Market Leade

1. QKS Group's SPARK Matrix™: Data Science and Machine Learning Platforms research provides an in-depth global analysis of the rapidly evolving ecosystem of data-driven technologies. As organizations across industries prioritize digital transformation, the need for scalable, intelligent, and automated analytics platforms continues to rise. The DSML landscape has grown significantly, driven by innovations in artificial intelligence, cloud computing, automation, and enterprise analytics. QKS Group’s research offers technology vendors a strategic understanding of emerging trends and competitive dynamics while helping enterprise users evaluate the capabilities, strengths, and market positioning of different vendors. Evolving Market Landscape and Key Drivers The increasing adoption of AI and advanced analytics across sectors such as banking, healthcare, retail, manufacturing, telecommunications, and government has accelerated demand for integrated data science and machine learning platforms. Businesses are increasingly recognizing the need for platforms that not only support model development but also streamline the entire machine learning lifecycle—from data ingestion and preparation to model deployment and monitoring. Key market drivers include: •Explosive data growth supporting the need for scalable analytics tools. •Enterprise AI adoption, powering business automation and intelligent decision- making. •Rise of citizen data scientists, increasing demand for intuitive, low-code and no-code platforms. •Cloud migration, enabling companies to leverage flexible, cost-effective computing resources. •Regulatory pressures, requiring robust model governance and transparency. These drivers collectively shape the future of DSML platforms as essential enterprise technologies for innovation and competitive advantage. Comprehensive Competitive Landscape Through SPARK Matrix™ A significant highlight of QKS Group’s research is the proprietary SPARK Matrix™ analysis, an expert-driven benchmarking methodology that evaluates vendors based on technology excellence and market impact. The SPARK Matrix provides a visual representation of market positioning, helping organizations understand vendor differentiation, capabilities, and technological maturity. The evaluation covers both established giants and emerging innovators, reflecting the diversity and competitiveness of the global DSML ecosystem. Vendors analyzed in the

2. SPARK Matrix include:4Paradigm, Altair, Alteryx (Siemens), Anaconda, AWS, Cloudera, DataBricks, Dataiku, DataRobot, Domino Data Lab, dotData, Google, H2O.ai, Iguazio (McKinsey), IBM, KNIME, MathWorks, Microsoft, Posit, Samsung SDS, SAS, and Tellius. Each vendor is assessed on parameters such as automation capabilities, scalability, deployment flexibility, AI integration, governance, MLOps maturity, and support for end- to-end data science workflows. Integrated Capabilities Powering Modern DSML Platforms Modern SPARK Matrix™: Data Science and Machine Learning Platforms have evolved into fully integrated ecosystems that support the entire lifecycle of machine learning and analytics. Today's platforms offer: 1. Unified Data Ingestion and Preparation DSML platforms enable seamless integration with databases, data lakes, cloud storage, APIs, and real-time data streams. Advanced preprocessing, transformation, and feature engineering tools help teams prepare high-quality input data efficiently. 2. End-to-End Machine Learning Model Development Through a combination of code-first notebooks and low-code interfaces, platforms support: •Model training •Feature engineering •Model validation •Hyperparameter tuning •Collaborative experimentation This versatility empowers both data scientists and business analysts to build models effectively. 3. Automation Through AutoML AutoML capabilities reduce manual workload by automating: •Algorithm selection •Feature engineering •Model optimization •Performance evaluation

3. This democratizes machine learning and accelerates model creation for non-expert users. 4. MLOps for Production Deployment and Monitoring MLOps has become critical for scaling AI across enterprises. DSML platforms incorporate: •Continuous model training (CT) •Continuous integration/continuous deployment (CI/CD) •Automated model monitoring •Drift detection •Governance and compliance tools These capabilities ensure reliability, transparency, and long-term model performance. 5. Scalability and Flexibility With cloud-native designs, DSML platforms efficiently manage large datasets and computationally intensive workloads. Support for hybrid and on-premises deployments ensures flexibility aligned with organizational requirements. 6. Collaboration and Governance Tools Given the collaborative nature of SPARK Matrix™: Data Science and Machine Learning Platforms now include role-based access controls, versioning, reproducibility tools, and centralized model repositories. These features strengthen team productivity while ensuring compliance and governance. The next generation of platforms will not only support machine learning but will operate as enterprise-wide AI fabric powering decision-making across every department. These platforms incorporate MLOps for continuous monitoring, scalability for handling large datasets, and automation through Atol to streamline workflows. Supporting both code-based and low-code tools, DSML platforms ensure reproducibility, governance, and collaboration while integrating with cloud and on-premises infrastructure to enable AI-driven decision-making at scale.”