Machine learning
SmartObserve offers two distinct approaches to machine learning (ML): using ML models for tasks like semantic search and text generation, and running statistical algorithms for data analysis. Choose the approach that best fits your use case.
Interactive demos
ML models for search and AI/ML-powered applications
SmartObserve supports ML models that you can use to enhance search relevance through semantic understanding. You can either deploy models directly within your SmartObserve cluster or connect to models hosted on external platforms. These models can transform text into vector embeddings, enabling semantic search capabilities, or provide advanced features like text generation and question answering. For more information, see Integrating ML models.
SmartObserve Assistant and automation
SmartObserve Assistant Toolkit helps you create AI-powered assistants for SmartObserve Dashboards.
Built-in algorithms for data analysis
SmartObserve includes built-in algorithms that analyze your data directly within your cluster, enabling tasks like anomaly detection, data clustering, and predictive analytics without requiring external ML models.