🚨 ALERT: End of support notice: On October 31, 2025, AWS will discontinue support for Amazon Lookout for Vision. After October 31, 2025, you will no longer be able to access the Lookout for Vision console or Lookout for Vision resources. For more information, visit this blog post. 🚨

Using the Lookout for Vision models and migrating off Amazon Lookout for Vision

Check this notebook for a walkthrough on training classification or segmentation models on a Amazon SageMaker Jupyter notebook. This demo uses the sample cookie data included here to train on with the package available on Amazon Sagemaker Marketplace (Algorithms) and package up a model for use.

Lookout for Vision Workshop - DEPRECATED

⚠️ To migrate from LFV to SageMaker algorithm, STOP reading and following notebook in this folder: https://github.com/aws-samples/amazon-lookout-for-vision/tree/main/computer-vision-defect-detection

Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. This workshop is intended to run through an end to end example of using the boto3 python SDK to train and host Lookout for Vision models. For a console-only getting started guide, see https://docs.aws.amazon.com/lookout-for-vision/latest/developer-guide/getting-started.html

Steps:

1. Launch this cloudformation template - (Make sure you are in us-east-1)

2. Navigate to SageMaker on your AWS console and click Notebook instances on the side menu

3. Click "Open Jupyter" as shown below

4. Click to open the "Amazon Lookout for Vision Lab.ipynb" notebook

4. Follow instructions in the notebook to complete the lab

5. As you go through steps in the notebook, notice changes in the console, specifically when:

• Creating a project
• Creating a dataset
• Training your model
• Evaluating your model
• Using your model

5. For EDGE Deployment, see README.md in the edge/ directory tree for further instructions