Favorite This is a guest post from Mikael Graindorge, Sales Operations Leader at Thermo Fisher Scientific. In the life sciences industry, data is growing in abundance and is getting increasingly complex, which makes it challenging to use traditional analytics methodologies. At Thermo Fisher Scientific, our mission is to make the

Read More
Shared by AWS Machine Learning September 1, 2021

Favorite With the number of cameras and sensors deployed growing exponentially, companies across industries are consuming more video than ever before. Additionally, advancements in analytics have expanded potential use cases, and these devices are now used to improve business operations and intelligence. In turn, the ability to effectively process video

Read More
Shared by AWS Machine Learning August 28, 2021

Favorite We are excited to announce Aria, Amazon Polly’s first New Zealand English Neural text-to-speech (NTTS) voice. Similar to other Amazon Polly voices, Aria is developed as a voice that sounds bright, natural, and upbeat. This new voice for Aotearoa (New Zealand in Māori) is uniquely Kiwi. It includes a

Read More
Shared by AWS Machine Learning August 28, 2021

Favorite The celebrity recognition feature in Amazon Rekognition automatically recognizes tens of thousands of well-known personalities in images and videos using machine learning (ML). Celebrity recognition significantly reduces the repetitive manual effort required to tag produced media content and make it readily searchable. Starting today, we’re updating our models to

Read More
Shared by AWS Machine Learning August 27, 2021

Favorite With Amazon SageMaker Pipelines, you can create, automate, and manage end-to-end machine learning (ML) workflows at scale. SageMaker Projects build on SageMaker Pipelines by providing several MLOps templates that automate model building and deployment pipelines using continuous integration and continuous delivery (CI/CD). To help you get started, SageMaker Pipelines

Read More
Shared by AWS Machine Learning August 26, 2021

Favorite Multiple pieces of information are often required to complete a task or to process a query. For example, when talking to an insurance agent, a caller might ask, “Can you provide me quotes for home, auto, and boat?” The agent recognizes this as a list of policy types before

Read More
Shared by AWS Machine Learning August 25, 2021

Favorite Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning. It provides a single, web-based visual interface where you can perform all ML development steps required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move

Read More
Shared by AWS Machine Learning August 25, 2021

Favorite Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model

Read More
Shared by AWS Machine Learning August 19, 2021

Favorite In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to

Read More
Shared by AWS Machine Learning August 19, 2021

Favorite Today, we’re excited to announce that Amazon SageMaker notebook instances support Amazon Linux 2. You can now choose Amazon Linux 2 for your new SageMaker notebook instance to take advantage of the latest update and support provided by Amazon Linux 2. SageMaker notebook instances are fully managed Jupyter Notebooks

Read More
Shared by AWS Machine Learning August 19, 2021