AWS Machine Learning Blog
Category: Advanced (300)
Intelligent document processing with AWS AI services: Part 1
Organizations across industries such as healthcare, finance and lending, legal, retail, and manufacturing often have to deal with a lot of documents in their day-to-day business processes. These documents contain critical information that are key to making decisions on time in order to maintain the highest levels of customer satisfaction, faster customer onboarding, and lower […]
Scale YOLOv5 inference with Amazon SageMaker endpoints and AWS Lambda
After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Amazon SageMaker endpoints provide an easily scalable […]
Simplify iterative machine learning model development by adding features to existing feature groups in Amazon SageMaker Feature Store
Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which is a purpose-built, fully managed, centralized […]
Build and train ML models using a data mesh architecture on AWS: Part 2
This is the second part of a series that showcases the machine learning (ML) lifecycle with a data mesh design pattern for a large enterprise with multiple lines of business (LOBs) and a Center of Excellence (CoE) for analytics and ML. In part 1, we addressed the data steward persona and showcased a data mesh […]
Build and train ML models using a data mesh architecture on AWS: Part 1
Organizations across various industries are using artificial intelligence (AI) and machine learning (ML) to solve business challenges specific to their industry. For example, in the financial services industry, you can use AI and ML to solve challenges around fraud detection, credit risk prediction, direct marketing, and many others. Large enterprises sometimes set up a center […]
Build a mental health machine learning risk model using Amazon SageMaker Data Wrangler
This post is co-written by Shibangi Saha, Data Scientist, and Graciela Kravtzov, Co-Founder and CTO, of Equilibrium Point. Many individuals are experiencing new symptoms of mental illness, such as stress, anxiety, depression, substance use, and post-traumatic stress disorder (PTSD). According to Kaiser Family Foundation, about half of adults (47%) nationwide have reported negative mental health […]