Category: Technical How-to

Natural language processing (NLP) is the field in machine learning (ML) concerned with giving computers the ability to understand text and spoken words in the same way as human beings can. Recently, state-of-the-art architectures like the transformer architecture are used to achieve near-human performance on NLP downstream tasks like text summarization, text classification, entity recognition, […]

In this post, we present a cross-account observability dashboard that provides a centralized view for monitoring SageMaker user activities and resources across multiple accounts. It allows the end-users and cloud management team to efficiently monitor what ML workloads are running, view the status of these workloads, and trace back different account activities at certain points of time.

Creative advertising has the potential to be revolutionized by generative AI (GenAI). You can now create a wide variation of novel images, such as product shots, by retraining a GenAI model and providing a few inputs into the model, such as textual prompts (sentences describing the scene and objects to be produced by the model). […]

Amazon SageMaker offers several ways to run distributed data processing jobs with Apache Spark, a popular distributed computing framework for big data processing. You can run Spark applications interactively from Amazon SageMaker Studio by connecting SageMaker Studio notebooks and AWS Glue Interactive Sessions to run Spark jobs with a serverless cluster. With interactive sessions, you […]

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. An enterprise search solution should […]

Training and serving thousands of models requires a robust and scalable infrastructure, which is where Amazon SageMaker can help. SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy ML models quickly, while also offering the cost-saving benefits of using the AWS Cloud infrastructure. In this post, we explore how you can use SageMaker features, including Amazon SageMaker Processing, SageMaker training jobs, and SageMaker multi-model endpoints (MMEs), to train and serve thousands of models in a cost-effective way. To get started with the described solution, you can refer to the accompanying notebook on GitHub.

Generative AI has become a common tool for enhancing and accelerating the creative process across various industries, including entertainment, advertising, and graphic design. It enables more personalized experiences for audiences and improves the overall quality of the final products. One significant benefit of generative AI is creating unique and personalized experiences for users. For example, […]

In today’s rapidly evolving healthcare landscape, doctors are faced with vast amounts of clinical data from various sources, such as caregiver notes, electronic health records, and imaging reports. This wealth of information, while essential for patient care, can also be overwhelming and time-consuming for medical professionals to sift through and analyze. Efficiently summarizing and extracting […]

Drug development is a complex and long process that involves screening thousands of drug candidates and using computational or experimental methods to evaluate leads. According to McKinsey, a single drug can take 10 years and cost an average of $2.6 billion to go through disease target identification, drug screening, drug-target validation, and eventual commercial launch. […]

If you are a business analyst, understanding customer behavior is probably one of the most important things you care about. Understanding the reasons and mechanisms behind customer purchase decisions can facilitate revenue growth. However, the loss of customers (commonly referred to as customer churn) always poses a risk. Gaining insights into why customers leave can […]