Editorial Summary :

Machine learning (ML) developer persona needs tooling around experiment tracking, lineage, and collaboration . In this post, we train a model to identify objects for an autonomous vehicle use case using Weights & Biases (W&B) and Amazon SageMaker . The joint solution reduces manual work for the ML developer, creates more transparency in the model development process, and enables teams to collaborate on projects . To get started with Studio, you need an AWS account and an IAM user or role with permissions to create a Studio domain . Weights & Biases (wandb) is a standard Python library . We use the Cambridge-driving Labeled Video Database (CamVid) for this example . It contains a collection of videos with object class semantic labels, complete with metadata . The database provides ground truth labels that associate each pixel with one of 32 semantic classes . We can version our dataset as a wandb.Artifact, that way we can reference it later . You can also create a lifecycle configuration to automatically install the packages every time you start the PyTorch app . With Weights & Biases, you can easily create dashboards with summaries of your experiments to quickly analyze training results . To improve the performance of the baseline model, we need to select the best model and the best set of hyperparameters to train . W&B is especially useful when assessing model performance by using the power of wandb.Tables to visualize where our model is doing badly . In the following table, we first filter by choosing where the Dice score is positive (pedestrians are present in the image) Then we sort in ascending order to identify our worst-detected pedestrians . This post showcased the Weights & Biases MLOps platform, how to set up W&B in SageMaker Studio, and how to run an introductory notebook on the joint solution . Weights and Biases is a free service that uses AI and MLOPs to solve short-term forecasting for solar energy . To try the service for free, sign up or visit the WandB AWS Marketplace listing . If you’re interested in learning more, you can access the live report .

Key Highlights :

  • Weights & Biases helps ML teams build better models faster .
  • Weights and Biases and Amazon SageMaker are the first fully integrated development environment (IDE) for ML .
  • Weights & Biases (wandb) is a standard Python library .
  • We use the Cambridge-driving Labeled Video Database (CamVid) for this example .
  • Weights & Biases is especially useful when assessing model performance by using the power of wandb.Tables to visualize where our model is doing badly .
  • Weights & Biases is a free service that uses AI and MLOPs to solve problems in cars .
  • This post showcases the Weights and Biases MLOP platform and how to use it in SageMaker Studio .

The editorial is based on the content sourced from aws.amazon.com

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