Fiddler for SageMaker Partner AI Apps

The Fiddler AI Observability Platform serves as a single pane of glass for Data Science, Engineering, and Line-of-Business teams to validate, monitor, and analyze ML models in production (including tabular, deep learning, computer vision, and natural language processing) so that enterprises deploying ML models on Amazon SageMaker could leverage Fiddler to build high-performing, accurate, and responsible AI applications. Fiddler and Amazon SageMaker together provide a powerful, unified MLOps solution to operationalize ML models, enabling enterprises to deploy, monitor, analyze, and improve ML applications.

Validate: Ship more models into production and increase confidence in your models' outputs. Validate and improve models before deployment for optimal outcomes.

Monitoring: Fiddler helps you reduce costs and increase efficiencies by tracking how all your tabular, natural language processing (NLP), computer vision (CV), and deep learning ML deployments are performing. Identify and mitigate model performance, drift, or data integrity issues before they impact your business.

Analytics: Fiddler's model analytics tool helps you gain actionable insights from your models to make data-driven business decisions. Understand trends and outliers at the macro-level using 3D UMAP and perform root cause analysis and diagnostics to understand the micro-level issues impacting your ML application. Pinpoint specific areas to optimize by drilling down on segments of interest.

Fairness: Build transparent, accountable, and ethical practices for your business with responsible AI. Fiddler increases visibility in AI governance with continuous monitoring, while detecting and mitigating bias in datasets and models.

Enterprise-level scale: Fiddler helps companies securely operationalize and scale model deployment across their ML lifecycle. Fiddler provides SOC2 Type 2 security compliance and role-based access control (RBAC) so that users across the business receive level-specific permissions to access protected environments.

Start monitoring your ML models by simply uploading your baseline dataset and register your model with Fiddler. Publish production events and start analyzing your model's performance.