Remove Accountability Remove Automotive Remove Metrics Remove Scripts
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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts 104
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Schedule your notebooks from any JupyterLab environment using the Amazon SageMaker JupyterLab extension

AWS Machine Learning

Examples of such use cases include scaling up a feature engineering job that was previously tested on a small sample dataset on a small notebook instance, running nightly reports to gain insights into business metrics, and retraining ML models on a schedule as new data becomes available.

Scripts 78
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MLOps at the edge with Amazon SageMaker Edge Manager and AWS IoT Greengrass

AWS Machine Learning

Internet of Things (IoT) has enabled customers in multiple industries, such as manufacturing, automotive, and energy, to monitor and control real-world environments. Data scientists evaluate the metrics of multiple model versions and request the promotion of the best model to production by triggering the CI/CD pipeline.

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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning

Healthcare organizations must navigate strict compliance regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, while implementing FL solutions. FedML Octopus is the industrial-grade platform of cross-silo FL for cross-organization and cross-account training.

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Customer Rage Study Podcast: Interview With Scott Broetzmann and Mary Murcott

Connecting the Dots

Number 4: “Can I get your account information again?” – How many times have we heard that and just wanted to take the phone and throw it through the wall because you just spent 10 minutes entering in all of the information that they’re going to ask for again? You could take automotive for instance, quality there is really, really improved.

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Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS

AWS Machine Learning

This includes scripts for model loading, inference handling etc. He works with large Automotive customers as their trusted advisor to transform their Machine Learning workloads and migrate to the cloud. A list of models is available in the models_manifest.json file provided by JumpStart.

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Predict vehicle fleet failure probability using Amazon SageMaker Jumpstart

AWS Machine Learning

Predictive maintenance is critical in automotive industries because it can avoid out-of-the-blue mechanical failures and reactive maintenance activities that disrupt operations. The first thing we need to do before we can use any AWS services is to make sure we have signed up for and created an AWS account.