Compare Amazon Elastic Inference vs. Amazon SageMaker Feature Store

Amazon Elastic Inference provides an affordable way to enhance Amazon EC2 and Sagemaker instances or Amazon ECS tasks with GPU-powered acceleration, potentially cutting deep learning inference costs by as much as 75%. It is compatible with models built on TensorFlow, Apache MXNet, PyTorch, and ONNX. The term "inference" refers to the act of generating predictions from a trained model. In the realm of deep learning, inference can represent up to 90% of the total operational expenses, primarily for two reasons. Firstly, GPU instances are generally optimized for model training rather than inference, as training tasks can handle numerous data samples simultaneously, while inference typically involves processing one input at a time in real-time, resulting in minimal GPU usage. Consequently, relying solely on GPU instances for inference can lead to higher costs. Conversely, CPU instances lack the necessary specialization for matrix computations, making them inefficient and often too sluggish for deep learning inference tasks. This necessitates a solution like Elastic Inference, which optimally balances cost and performance in inference scenarios.

Amazon SageMaker Feature Store serves as a comprehensive, fully managed repository specifically designed for the storage, sharing, and management of features utilized in machine learning (ML) models. Features represent the data inputs that are essential during both the training phase and inference process of ML models. For instance, in a music recommendation application, relevant features might encompass song ratings, listening times, and audience demographics. The importance of feature quality cannot be overstated, as it plays a vital role in achieving a model with high accuracy, and various teams often rely on these features repeatedly. Moreover, synchronizing features between offline batch training and real-time inference poses significant challenges. SageMaker Feature Store effectively addresses this issue by offering a secure and cohesive environment that supports feature utilization throughout the entire ML lifecycle. This platform enables users to store, share, and manage features for both training and inference, thereby facilitating their reuse across different ML applications. Additionally, it allows for the ingestion of features from a multitude of data sources, including both streaming and batch inputs such as application logs, service logs, clickstream data, and sensor readings, ensuring versatility and efficiency in feature management. Ultimately, SageMaker Feature Store enhances collaboration and improves model performance across various machine learning projects.