Using artificial intelligence to detect product defects with AWS Step Functions

Factories that produce a high volume of inventory must ensure that defective products are not shipped. This is often accomplished with human workers on the assembly line or through computer vision.

You can build an application that uses a custom image classification model to detect and report back any defects in a product, then takes appropriate action. This method provides a powerful, scalable, and simple solution for quality control. It uses Amazon S3Amazon SQSAWS LambdaAWS Step Functions, and Amazon SageMaker.

To simulate a production scenario, the model is trained using an example dataset containing images of an open-source printed circuit board, with defects and without. An accompanying AWS Serverless Application Repository application deploys the Step Functions workflow for handling image classification and notifications.

https://aws.amazon.com/blogs/compute/using-artificial-intelligence-to-detect-product-defects-with-aws-step-functions/

Over 150 of the Best Machine Learning, NLP, and Python Tutorials

While machine learning has a rich history dating back to 1959, the field is evolving at an unprecedented rate. In a recent article, I discussed why the broader artificial intelligence field is booming and likely will for some time to come. Those interested in learning ML may find it daunting to get started.

https://medium.com/machine-learning-in-practice/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78#hn

Deploy machine learning models in production

Key features

  • Multi framework: Cortex supports TensorFlow, PyTorch, scikit-learn, XGBoost, and more.
  • Autoscaling: Cortex automatically scales APIs to handle production workloads.
  • CPU / GPU support: Cortex can run inference on CPU or GPU infrastructure.
  • Spot instances: Cortex supports EC2 spot instances.
  • Rolling updates: Cortex updates deployed APIs without any downtime.
  • Log streaming: Cortex streams logs from deployed models to your CLI.
  • Prediction monitoring: Cortex monitors network metrics and tracks predictions.
  • Minimal configuration: Cortex deployments are defined in a single cortex.yaml file.

https://github.com/cortexlabs/cortex

ID Card Digitization and Information Extraction using Deep Learning – A Review

In this article, we will discuss how any organisation can use deep learning to automate ID card information extraction, data entry and reviewing procedures to achieve greater efficiency and cut costs. We will review different deep learning approaches that have been used in the past for this problem, compare the results and look into the latest in the field. We will discuss graph neural networks and how they are being used for digitization.

While we will be looking at the specific use-case of ID cards, anyone dealing with any form of documents, invoices and receipts, etc and is interested in building a technical understanding of how deep learning and OCR can solve the problem will find the information useful.

https://nanonets.com/blog/id-card-digitization-deep-learning/

A Guide to S3 Batch on AWS

AWS just announced the release of S3 Batch Operations. This is a hotly-anticpated release that was originally announced at re:Invent 2018. With S3 Batch, you can run tasks on existing S3 objects. This will make it much easier to run previously difficult tasks like retagging S3 objects, copying objects to another bucket, or processing large numbers of objects in bulk.

In this post, we’ll do a deep dive into S3 Batch. You will learn when, why, and how to use S3 Batch. First, we’ll do an overview of the key elements involved in an S3 Batch job. Then, we’ll walkthrough an example by doing sentiment analysis on a group of existing objects with AWS Lambda and Amazon Comprehend.

https://www.alexdebrie.com/posts/s3-batch/