AWS Machine Learning

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 […]

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 […]

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 u […]

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 […]

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 […]

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 b […]

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 […]

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 […]

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 […]

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 a […]

Amazon SageMaker notebook instances now support Amazon Linux 2, so you can now create a new Amazon SageMaker notebook instance to start developing your machine learning (ML) models with the latest updates. An […]

Data science workflows have to pass multiple stages as they progress from the experimentation to production pipeline. A common approach involves separate accounts dedicated to different phases of the AI/ML […]

If you’re integrating your Amazon Lex chatbots with Slack, chances are you’ll come across Block Kit. Block Kit is a UI framework for Slack apps. Like response cards, Block Kit can help simplify interactions wit […]

Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can […]

Amazon Personalize now enables you to optimize personalized recommendations for a business metric of your choice, in addition to improving relevance of recommendations for your users. You can define a business […]

As customers continue to come up with new use-cases for machine learning, data gravity is as important as ever. Where latency and network connectivity is not an issue, generating data in one location (such as a […]

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models built on different frameworks. SageMaker […]

You can use AWS Snowball Edge devices in locations like cruise ships, oil rigs, and factory floors with limited to no network connectivity for a wide range of machine learning (ML) applications such as […]

In a machine learning (ML) journey, one crucial step before building any ML model is to transform your data and design features from your data so that your data can be machine-readable. This step is known as […]

This is a guest post by Ernesto DiMarino, who is Head of Enterprise Applications and Data at Cortica.
Cortica is on a mission to revolutionize healthcare for children with autism and other neurodevelopmental […]