This chapterDeep learning offers an in-depth examination of the frameworksFrameworks and tools related to deep learningDeep learning that are important for modern object detection. The primary focus will be on the two most prevalent ecosystems–TensorFlowTensorFlow and PyTorchPyTorch–and the common strengths for deployment to production and flexibility for research. Other specialized libraries, such as Detectron2Detectron2 and OpenVINO, are discussed as they support the object detection development process by simplifying model development and optimizing inference. This chapter builds onto the practical section regarding the value of pre-trained or transfer learningTransfer learning models which enable high performance using minimal data, and also highlights important strategies for deploying in real-world settings such as model compressionModel compression techniques (e.g. pruning and quantizationQuantization) for deployment on edge devicesEdge devices, and cloud deploymentCloud deployment strategiesDeployment strategies for serving any data. With this discussion, practitioners will understand when the models and accompanying tools/strategies can be integrated for building effective object detection systems in a range of situations from embedded devices to cloud-based infrastructure.