Favorite Artificial intelligence (AI) adoption is accelerating across industries and use cases. Recent scientific breakthroughs in deep learning (DL), large language models (LLMs), and generative AI is allowing customers to use advanced state-of-the-art solutions with almost human-like performance. These complex models often require hardware acceleration because it enables not only
Read More
Shared by AWS Machine Learning August 8, 2023
Favorite 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
Read More
Shared by AWS Machine Learning August 8, 2023
Favorite Data classification, extraction, and analysis can be challenging for organizations that deal with volumes of documents. Traditional document processing solutions are manual, expensive, error prone, and difficult to scale. AWS intelligent document processing (IDP), with AI services such as Amazon Textract, allows you to take advantage of industry-leading machine
Read More
Shared by AWS Machine Learning August 4, 2023
Favorite This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. We’re excited to announce Amazon SageMaker and Salesforce Data Cloud integration. With this capability, businesses can access their Salesforce data securely with a zero-copy approach using SageMaker and use SageMaker tools to build, train, and deploy
Read More
Shared by AWS Machine Learning August 4, 2023
Favorite This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the second post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. In Part 1, we show how the Salesforce Data Cloud and Einstein Studio integration with SageMaker allows businesses
Read More
Shared by AWS Machine Learning August 4, 2023
Favorite 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
Read More
Shared by AWS Machine Learning August 4, 2023
Favorite Data preparation is a critical step in any data-driven project, and having the right tools can greatly enhance operational efficiency. Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler,
Read More
Shared by AWS Machine Learning August 4, 2023
Favorite The Open Source Initiative (OSI) continues to celebrate the 25th anniversary of Open Source at the best tech conferences from around the world, promoting Open Source software and fostering collaboration within the global community. This past month, the OSI celebrated the anniversary in all corners of the world: at
Read More
Shared by voicesofopensource August 3, 2023
Favorite Posted by Greg Corrado, Head of Health AI, Google Research, and Yossi Matias, VP, Engineering and Research, Google Research Medicine is an inherently multimodal discipline. When providing care, clinicians routinely interpret data from a wide range of modalities including medical images, clinical notes, lab tests, electronic health records, genomics,
Read More
Shared by Google AI Technology August 3, 2023
Favorite Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon
Read More
Shared by AWS Machine Learning August 3, 2023
Shared by AWS Machine Learning August 8, 2023
Shared by 
Shared by 