Amazon SageMaker, a platform for building, training, and deploying machine learning models, can significantly reduce development time for generative AI and ML tasks. However, manual steps are still required for fine-tuning related services like queues and databases within inference pipelines. To address this, Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues, enabling efficient load testing and optimization of ML infrastructure. OLAF, available as an open-source tool, helps streamline the testing process, reducing time from a week to a few hours, and supports scalable deployment of ML models. This matters because it allows organizations to optimize their ML operations efficiently, saving time and resources while ensuring high performance.
