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Teradata ClearScape Analytics

Teradata ClearScape Analytics

Provides in-database tools to build, train, deploy, monitor, and manage ML/AI models at scale, supporting common model formats, AutoML, MLOps workflows, and cloud/lake integration.

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Teradata ClearScape Analytics is an advanced data analytics platform designed to streamline the development and deployment of machine learning (ML) and artificial intelligence (AI) workflows within enterprise environments. It provides end-to-end orchestration of ML pipelines, allowing businesses to build, manage, and monitor AI models efficiently without the need for complex data movement. The platform supports scalable execution through a massively parallel processing (MPP) engine, enabling horizontal scaling for large datasets and complex analytics.

Key features include comprehensive AI integration capabilities with support for various model formats such as PMML, ONNX, and MOJO, automated model versioning, and real-time monitoring of model performance metrics like concept and data drift. ClearScape Analytics also offers AutoML pipelines that automate feature engineering, algorithm selection, and hyperparameter tuning, optimized for large-scale data environments. The drag-and-drop MLOps interface simplifies the deployment of champion and challenger models, enhancing operational efficiency for data scientists.

Furthermore, ClearScape Analytics integrates with cloud-native architectures like Teradata VantageCloud Lake, extending its capabilities for flexible, multi-cloud data access and lakehouse environments. This integration facilitates seamless unified analytics across diverse data sources and supports hybrid AI workflows that combine relational data with embedding-based semantic search and generative AI tasks.

Overall, Teradata ClearScape Analytics provides a robust, scalable solution for enterprises seeking to operationalize AI and machine learning at scale, offering advanced orchestration, monitoring, and deployment features tailored to complex, regulated industries and diverse data ecosystems. It supports a full lifecycle approach to AI model management, blending analytical rigor with enterprise governance and cloud flexibility.

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