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Favorite 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
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Shared by AWS Machine Learning November 3, 2022
Favorite 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
Read More Shared by AWS Machine Learning November 2, 2022
Favorite 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,
Read More Shared by AWS Machine Learning November 1, 2022
Favorite 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
Read More Shared by AWS Machine Learning November 1, 2022
Favorite 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
Read More Shared by AWS Machine Learning November 1, 2022
Favorite 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
Read More Shared by AWS Machine Learning October 31, 2022
Favorite Amazon SageMaker JumpStart provides pre-trained, open-source models for a wide range of problem types to help you get started with machine learning (ML). JumpStart also provides solution templates that set up infrastructure for common use cases, and executable example notebooks for ML with Amazon SageMaker. As a business user,
Read More Shared by AWS Machine Learning October 31, 2022
Favorite 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
Read More Shared by AWS Machine Learning October 31, 2022
Favorite With environmental, social, and governance (ESG) initiatives becoming more important for companies, our customer, one of Greater China region’s top convenience store chains, has been seeking a solution to reduce food waste (currently over $3.5 million USD per year). Doing so will allow them to not only realize substantial
Read More Shared by AWS Machine Learning October 27, 2022
Favorite 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
Read More Shared by AWS Machine Learning October 27, 2022