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Demo: Image classification - Default detection

Deep Learning using Databricks Lakehouse: detect defaults in PCBs with Hugging Face transformers and PyTorch Lightning.

Being able to analyze factory default in real time is a critical task to increase production line quality and reducing defaults.
Implementing such a use case with deep learning for computer vision can be challenging at scale, especially when it comes to data preprocessing and building production-grade pipelines.
Databricks simplifies this process end to end, making all the operational tasks simple so that you can focus on improving the model performance.
In this demo, we will cover how to implement a complete deep learning pipeline to detect printed circuit board (PCB) defaults, from the image ingestion to real-time inferences (over REST API):


  • Simplify data and image ingestions using Databricks Auto Loader and Delta Lake
  • Learn how to do image preprocessing at scale
  • Train and deploy a computer vision pipeline with Hugging Face and the new Spark DataFrame data set for transformers
  • Deploy the pipeline for batch or streaming inferences and real-time serving with Databricks Serverless model endpoints
  • Understand which pixels are flagged as damaged PCBs to highlight potential default
  • A complete training and inference example using PyTorch Lightning if the Hugging Face library isn’t enough for your requirements, including deltatorch and distributed training with TorchDistributor

Installing the demos

%pip install dbdemos
import dbdemos
dbdemos.install('computer-vision-pcb')


Databricks demos components

Delta Live Table
Data Science
Unity Catalog
BI/DW/DBSQL

Assets available in the Databricks demo