Ground Truth Object Detection Tutorial is a similar end-to-end example but for an object detection task.From Unlabeled Data to a Deployed Machine Learning Model: A SageMaker Ground Truth Demonstration for Image Classification is an end-to-end example that starts with an unlabeled dataset, labels it using the Ground Truth API, analyzes the results, trains an image classification neural net using the annotated dataset, and finally uses the trained model to perform batch and online inference.Bring your own model for SageMaker labeling workflows with active learning is an end-to-end example that shows how to bring your custom training, inference logic and active learning to the Amazon SageMaker ecosystem. These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth. Introduction to Ground Truth Labeling Jobs SageMaker Pipelines with Amazon SageMaker Geospatial Capabilities shows how a geospatial data processing workflow can be automated by using an Amazon SageMaker Pipeline.Monitoring Glacier Melting with SageMaker Geospatial Capabilities shows how to monitor glacier melting at Mount Shasta using SageMaker geospatial capabilities. How to use Vector Enrichment Jobs for Reverse Geocoding shows how to use Amazon SageMaker Geospatial capabilities for reverse geocoding to obtain human readable addresses from data with latitude/longitude information.How to use Vector Enrichment Jobs for Map Matching shows how to use vector enrichtment operations with Amazon SageMaker Geospatial capabilities to snap GPS coordinates to road segments.Assess wildfire damage with Amazon SageMaker Geospatial Capabilities demonstrates how Amazon SageMaker geospatial capabilities can be used to identify and assess vegetation loss caused by the Dixie wildfire in Northern California.Digital Farming with Amazon SageMaker Geospatial Capabilities shows how geospatial capabilities can help accelerating, optimizing, and easing the processing of the geospatial data for the Digital Farming use cases.Monitoring Lake Drought with SageMaker Geospatial Capabilities shows how to monitor Lake Mead drought using SageMaker geospatial capabilities.These examples introduce SageMaker geospatial capabilities which makes it easy to build, train, and deploy ML models using geospatial data. □ Examples Introduction to geospatial capabilities See our announcement for details and how to update your existing clone. They can be accessed by clicking on the SageMaker Examples tab in Jupyter or the SageMaker logo in JupyterLab.Īlthough most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).Īs of February 7, 2022, the default branch is named "main". These example notebooks are automatically loaded into SageMaker Notebook Instances. The quickest setup to run example notebooks includes: The SageMaker example notebooks are Jupyter notebooks that demonstrate the usage of Amazon SageMaker. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. □ BackgroundĪmazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. * add CI badges, download model from s3, end2end run fixesĮxample Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker. * black-nb formatting on featurizer.ipynb * updated s3 url for dataset to sagemaker-example-files-prod-REGION * update wsgi.py to import preprocessing.py as app * Replace pickle.load with joblib.load, update Dockerfile * updated images to reflect Dockerfile and script name * updated README.md, renamed inference.py to preprocessing.py * Markdown fixes for predictor, serial-inf-pipelines * Markdown fixes - renamed featurizer to preprocessor * rename request_body var to input_data in featurizer/code/inference.py * Markdown fixes - dbl quote featurizer, add links to xgboost, sklearn Falcon BYOC xgboost - first commit ( #3982 ) * Falcon BYOC xgboost - first commit
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