Welcome to the first part in our three part series on YOLOv8! In this series, we'll show you how to work with YOLOv8, from downloading the off-the-shelf models, to fine-tuning these models for specific use cases, and everything in between. Throughout the series, we will be using two libraries: FiftyOne, the open source computer vision toolkit, and Ultralytics, the library that will give us access to YOLOv8. In Part 1, you'll learn how to generate, load, and visualize YOLOv8 predictions. In Part 2, we'll show you how to evaluate the quality of YOLOv8 model predictions.

Amazon SageMaker Neo was launched at AWS re:Invent 2018. It made notable performance improvement on models with statically known input and output data shapes, typically image classification models. These models are usually composed of a stack of blocks that contain compute-intensive operators, such as convolution and matrix multiplication. Neo applies a series of optimizations to boost the model's performance and reduce memory usage. The static feature significantly simplifies the compilation, and you can decide on runtime inference tasks such as memory sizes ahead of time using a dedicated analysis pass.