AWS Startups Blog
Automating Unstructured Data Processing with Amazon SageMaker
The super.AI platform helps customers to transform processes involving unstructured data such as images, videos, text, documents, and audio and automate them using a combination of AI, software, and humans. Their customers requested a more efficient, highly accurate labeling mechanism, so they eleased a new feature where the pipeline pre-processes data points using an ML model running on Amazon SageMaker.
Monitoring SageMaker ML Models with WhyLabs
As the real-world changes, machine learning models degrade in their ability to accurately represent it, resulting in model performance degradation. That’s why it’s important for data scientists and machine learning engineers to support models with tools that provide ML monitoring and observability, thereby preventing that performance degradation. In this post, we dive into the WhyLabs AI Observatory, a data and ML monitoring and observability platform, and show how it complements Amazon SageMaker.