Category: Amazon SageMaker

AWS is excited to announce the general availability of Amazon SageMaker integration in QuickSight. You can now integrate your own Amazon SageMaker ML models with QuickSight to analyze the augmented data and use it directly in your business intelligence dashboards. As a business analyst, data engineer, or data scientist, you can perform ML inference in […]

Amazon Web Services (AWS) is happy to announce the general availability of Notebooks within Amazon SageMaker Studio. Amazon SageMaker Studio supports on-the-fly selection of machine learning (ML) instance types, optimized and pre-packaged Amazon SageMaker Images, and sharing of Jupyter notebooks. You can switch a notebook from using a kernel on one instance type to another, […]

Machine Learning (ML) impacts industries around the globe, from financial services industry (FSI) and manufacturing  to autonomous vehicles and space exploration. ML is no longer just an aspirational technology exclusive to academic and research institutions; it has evolved into a mainstream technology that has the potential to benefit organizations of all sizes. However, a lack […]

Battlesnake is an AI competition in which you build AI-powered snakes. Battlesnake’s rules are similar to the traditional snakes game. Your goal is to be the last surviving snake when competing against other snakes. Developers of all levels build snakes using techniques ranging from unique heuristic-based strategies to state-of-the-art deep reinforcement learning (RL) algorithms. You […]

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thompson Reuters, use Amazon SageMaker to remove the heavy lifting from each step of the […]

With Amazon SageMaker Ground Truth, you can easily and inexpensively build accurately labeled machine learning (ML) datasets. To decrease labeling costs, SageMaker Ground Truth uses active learning to differentiate between data objects (like images or documents) that are difficult and easy to label. Difficult data objects are sent to human workers to be annotated and […]

As a multiplayer game publisher, you may often need to either over-provision resources or manually manage compute allocation when launching or maintaining an online game to avoid long player wait times. You need to develop, configure, and deploy tools that help you monitor and control the compute allocation. This post demonstrates GameServer Autopilot, a new […]

This post is co-written by Alexander Carlson, a machine learning engineer at Autodesk. Autodesk started its digital transformation journey years ago by moving workloads from private data centers to AWS services. The benefits of digital transformation are clear with generative design, which is a new technology that uses cloud computing to accelerate design exploration beyond […]

In the past decade, deep learning has advanced many different areas, such as computer vision and natural language processing. State-of-the-art models now achieve near-human performance in tasks such as image classification. Deep neural networks can achieve this because they consist of millions of parameters that you train on large training datasets. For instance, the BERT […]

Note: Amazon Elastic Inference is no longer available. Please see Amazon SageMaker for similar capabilities. When running deep learning models in production, balancing infrastructure cost versus model latency is always an important consideration. At re:Invent 2018, AWS introduced Amazon SageMaker Neo and Amazon Elastic Inference, two services that can make models more efficient for deep […]