AI Engineer: Ensuring Model Performance, Accuracy, and Reliability

Challenges

AI Engineers are tasked with developing, deploying, and monitoring LLMs to ensure they perform optimally and deliver consistent results. They need robust tools to track model behaviors, manage resource usage, and detect issues before they impact operations.

How Rakuten Sixthsense Helps

3D Latent Space Visualization

Enables engineers to visualize model behavior in a 3D space, identifying clusters and patterns for root cause analysis. This feature helps them optimize model performance and troubleshoot anomalies quickly, ensuring reliable output.

TraceSteps for Model Debugging

Tracks each step in the LLM's decision-making process, providing insights into model logic and generation pathways. This transparency allows engineers to identify issues and make refinements, improving model efficiency and accuracy.

Real-Time Security & Compliance Checks

Continuously monitors LLM interactions for security risks, such as unauthorized access and data breaches, ensuring model applications meet security and compliance standards without disrupting development cycles.

Data Drift Detection & Mitigation

Detects and addresses data drift, ensuring models adapt to evolving data patterns. This feature supports engineers in maintaining model accuracy over time, so their deployments remain robust and reliable.