Today, we’re pleased to announce Amazon SageMaker Training Compiler, a new Amazon SageMaker capability that can accelerate the training of deep learning (DL) models by up to 50%. As DL models grow in complexity, so too does the time it can take to optimize and train them. For example, it can take 25,000 GPU-hours to train popular natural language processing (NLP) model “RoBERTa“. Although there are techniques and optimizations that customers can apply to reduce the time it can take to train a model, these also take time to implement and require a rare skillset. This can impede innovation and progress in the wider adoption of artificial intelligence (AI). How has this been done to date? Typically, there are three…
Category: AWS
Reposts from Amazon Web Services (AWS).
New – Create and Manage EMR Clusters and Spark Jobs with Amazon SageMaker Studio
Today, we’re very excited to offer three new enhancements to our Amazon SageMaker Studio service. As of now, users of SageMaker Studio can create, terminate, manage, discover, and connect to Amazon EMR clusters running within a single AWS account and in shared accounts across an organization—all directly from SageMaker Studio. Furthermore, SageMaker Studio Notebook users can able to utilize SparkUI to monitor and debug Spark jobs running on an Amazon EMR cluster—directly from the SageMaker Studio Notebooks! The story so far… Before today, SageMaker Studio users had some ability to find and connect with EMR clusters, provided that they were running in the same account as SageMaker Studio. While useful in many circumstances, if a cluster did not exist that…
Announcing Amazon SageMaker Ground Truth Plus
Today, we’re pleased to announce the latest service in the Amazon SageMaker suite that will make labeling datasets easier than ever before. Ground Truth Plus is a turn-key service that uses an expert workforce to deliver high-quality training datasets fast, and reduces costs by up to 40 percent. The Challenges of Machine Learning Model Creation One of the biggest challenges in building and training machine learning (ML) models is sourcing enough high-quality, labeled data at scale to feed into and train those models so that they can make an accurate prediction. On the face of it, labeling data might seem like a fairly straightforward task… Step 1: Get data Step 2: Label it …but this is far from the reality.…
New DynamoDB Table Class – Save Up To 60% in Your DynamoDB Costs
Today we are announcing Amazon DynamoDB Standard-Infrequent Access (DynamoDB Standard-IA). A new table class for DynamoDB that reduces storage costs by 60 percent compared to existing DynamoDB Standard tables, and that delivers the same performance, durability, and scaling. Nowadays, many customers are moving their infrequently accessed data between DynamoDB and Amazon Simple Storage Service (Amazon S3). This means that customers are developing a process to migrate the data and build complex applications that must support two different APIs—one for DynamoDB and another for Amazon S3. DynamoDB Standard-IA table class is designed for customers who want a cost-optimized solution for storing infrequently accessed data in DynamoDB without changing any application code. Using this new table class, you get the single-digit millisecond…