AWS Machine Learning Blog
Category: Artificial Intelligence
Create random and stratified samples of data with Amazon SageMaker Data Wrangler
In this post, we walk you through two sampling techniques in Amazon SageMaker Data Wrangler so you can quickly create processing workflows for your data. We cover both random sampling and stratified sampling techniques to help you sample your data based on your specific requirements. Data Wrangler reduces the time it takes to aggregate and […]
Part 4: How NatWest Group migrated ML models to Amazon SageMaker architectures
The adoption of AWS cloud technology at NatWest Group means moving our machine learning (ML) workloads to a more robust and scalable solution, while reducing our time-to-live to deliver the best products and services for our customers. In this cloud adoption journey, we selected the Customer Lifetime Value (CLV) model to migrate to AWS. The […]
Part 3: How NatWest Group built auditable, reproducible, and explainable ML models with Amazon SageMaker
This is the third post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. This post is intended for data scientists, MLOps engineers, and data engineers who are interested in building ML pipeline templates with Amazon SageMaker. […]
Part 2: How NatWest Group built a secure, compliant, self-service MLOps platform using AWS Service Catalog and Amazon SageMaker
This is the second post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. In this post, we share how the NatWest Group utilized AWS to enable the self-service deployment of their standardized, secure, and compliant MLOps […]
Part 1: How NatWest Group built a scalable, secure, and sustainable MLOps platform
This is the first post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS to build a scalable, secure, and sustainable machine learning operations (MLOps) platform. This initial post provides an overview of the AWS and NatWest Group joint team implemented Amazon SageMaker Studio as the standard for […]
Accelerate data preparation with data quality and insights in Amazon SageMaker Data Wrangler
Amazon SageMaker Data Wrangler is a new capability of Amazon SageMaker that helps data scientists and data engineers quickly and easily prepare data for machine learning (ML) applications using a visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Today, […]
Host Hugging Face transformer models using Amazon SageMaker Serverless Inference
The last few years have seen rapid growth in the field of natural language processing (NLP) using transformer deep learning architectures. With its Transformers open-source library and machine learning (ML) platform, Hugging Face makes transfer learning and the latest transformer models accessible to the global AI community. This can reduce the time needed for data […]
How Nordic Aviation Capital uses Amazon Rekognition to streamline operations and save up to EUR200,000 annually
Nordic Aviation Capital (NAC) is the industry’s leading regional aircraft lessor, serving almost 70 airlines in approximately 45 countries worldwide. In 2021, NAC turned to AWS to help it use artificial intelligence (AI) to further improve its leasing operations and reduce its reliance on manual labor. With Amazon Rekognition Custom Labels, NAC built an AI […]
Secure AWS CodeArtifact access for isolated Amazon SageMaker notebook instances
AWS CodeArtifact allows developers to connect internal code repositories to upstream code repositories like Pypi, Maven, or NPM. AWS CodeArtifact is a powerful addition to CI/CD workflows on AWS, but it is similarly effective for code-bases hosted on a Jupyter notebook. This is a common development paradigm for Machine Learning developers that build and train […]
Specify and extract information from documents using the new Queries feature in Amazon Textract
Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. Amazon Textract now offers the flexibility to specify the data you need to extract from documents using the new Queries feature within the Analyze Document API. You don’t need to know the structure of the […]