AWS Machine Learning Blog

In Part 1 of this series, we discussed intelligent document processing (IDP), and how IDP can accelerate claims processing use cases in the insurance industry. We discussed how we can use AWS AI services to accurately categorize claims documents along with supporting documents. We also discussed how to extract various types of documents in an […]

The goal of intelligent document processing (IDP) is to help your organization make faster and more accurate decisions by applying AI to process your paperwork. This two-part series highlights the AWS AI technologies that insurance companies can use to speed up their business processes. These AI technologies can be used across insurance use cases such […]

To delight customers and minimize packaging waste, Amazon must select the optimal packaging type for billions of packages shipped every year. If too little protection is used for a fragile item such as a coffee mug, the item will arrive damaged and Amazon risks their customer’s trust. Using too much protection will result in increased […]

Intelligent document processing (IDP) has seen widespread adoption across enterprise and government organizations. Gartner estimates the IDP market will grow more than 100% year over year, and is projected to reach $4.8 billion in 2022. IDP helps transform structured, semi-structured, and unstructured data from a variety of document formats into actionable information. Processing unstructured data […]

Identifying, collecting, and transforming data is the foundation for machine learning (ML). According to a Forbes survey, there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. In addition, many of our customers face several challenges during the model operationalization phase […]

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best custom machine learning (ML) models based on your data. It’s an automated machine learning (AutoML) solution that eliminates the heavy lifting of handwritten ML models that requires ML expertise. Data scientists need to only provide a tabular dataset and select the target column to predict, […]

Relentless advancement in technology is improving the decision-making capacity of humans and enterprises alike. Digitization of the physical world has accelerated the three dimensions of data: velocity, variety, and volume. This has made information more widely available than before, allowing for advancements in problem-solving. Now, with cloud-enabled democratized availability, technologies like artificial intelligence (AI) and […]

Machine learning (ML) has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. With this rapid adoption of ML into every industry, companies are facing challenges in supporting low-latency predictions and with high availability while maximizing resource utilization […]

In the pursuit of superior accuracy, deep learning models in areas such as natural language processing and computer vision have significantly grown in size in the past few years, frequently counted in tens to hundreds of billions of parameters. Training these gigantic models is challenging and requires complex distribution strategies. Data scientists and machine learning […]

This post is co-written with Chaim Rand from Mobileye. Certain machine learning (ML) workloads, such as training computer vision models or reinforcement learning, often involve combining the GPU- or accelerator-intensive task of neural network model training with the CPU-intensive task of data preprocessing, like image augmentation. When both types of tasks run on the same […]