This repository contains code and materials for automating visual inspection tasks using AWS SageMaker. It showcases how to use machine learning models to detect defects or anomalies in images, specifically in industrial automation scenarios, such as quality control.

• dist/: Distribution-ready package for deployment.
• docs/: Documentation and guidelines for the project.
• lib/: Core libraries used in the project.
• notebooks/: Jupyter notebooks for data preparation, training, and model evaluation.
• src/: Source code for visual inspection automation.
• solution-assistant.zip: Pre-built solution package.
• stack_outputs.json: AWS CloudFormation stack output details.

You will need the following dependencies installed, which can be derived from the notebooks/ and src/ code:

• boto3: For AWS service interactions
• sagemaker: For using AWS SageMaker services
• numpy: Used for data manipulation
• Pandas: For data processing
• Matplotlib: For visualization
• TensorFlow or PyTorch (depending on the model used for training)

You can install these dependencies using:

pip install boto3 sagemaker numpy pandas matplotlib

1. Data Preparation: Use the provided notebooks in the notebooks/ folder to prepare your dataset for training the visual inspection model.
2. Model Training: Train models using AWS SageMaker by running the notebooks or scripts provided.
3. Deployment: Use the deployment scripts in dist/ and src/ to automate visual inspection tasks.

This project is licensed under the Apache-2.0 License. See the LICENSE file for more information.