SAS Visual Machine Learning is a comprehensive, cloud-native platform designed to streamline the entire analytics lifecycle, from data preparation to model deployment. It offers a user-friendly, low-code/no-code interface, enabling users of varying skill levels to build, manage, and deploy machine learning models efficiently. The platform supports a wide range of machine learning techniques, including deep learning, and integrates seamlessly with open-source tools like Python, R, and TensorFlow. Its scalable, in-memory processing ensures rapid analysis of large datasets, making it suitable for enterprise-level applications. Key Features and Functionality: - AutoML Capabilities: Automates feature engineering, model tuning, and selection, accelerating model development and enhancing performance. - Generative AI Integration: The upcoming SAS Viya Copilot will allow users to build models using conversational tools powered by generative AI. - Collaborative Environment: Supports multiple programming languages, including SAS, Python, R, and Lua, fostering collaboration among diverse teams. - Built-in Explainability: Provides interpretability reports with simple language explanations, aiding in understanding model decisions. - Interactive Data Visualization: Offers tools for data exploration and the creation of interactive reports through a self-service interface. - Bias Detection: Assesses models for performance and result biases relative to specified groups. - Cloud-Native Architecture: Ensures scalability and flexibility across various cloud environments. Primary Value and User Solutions: SAS Visual Machine Learning empowers organizations to harness the full potential of their data by simplifying the development and deployment of machine learning models. Its intuitive interface and automated features reduce the complexity and time required for model creation, enabling faster, data-driven decision-making. By supporting collaboration across different programming languages and providing built-in tools for model interpretability and bias detection, the platform ensures transparency and trust in AI-driven insights. Its scalability and cloud-native design make it an ideal solution for enterprises seeking to implement robust, efficient, and ethical machine learning practices.