This post is co-written with Thomas Capelle at Weights & Biases.
As more organizations use deep learning techniques such as computer vision and natural language processing, the machine learning (ML) developer persona needs scalable tooling around experiment tracking, lineage, and collaboration. Experiment tracking includes metadata such as operating system, infrastructure used, library, and input and output datasets—often tracked on a spreadsheet manually. Lineage involves tracking the datasets, transformations, and algorithms used to create an ML model. Collaboration includes ML developers working on a single project and also ML developers sharing their results across teams and to business stakeholders—a process commonly done via email, screenshots, and PowerPoint presentations.
In this post, we train a model to identify objects for an autonomous vehicle use case using Weights & Biases (W&B) and Amazon SageMaker. We showcase how the joint solution reduces manual work for the ML developer, creates more transparency in the model

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