Our mission at AWS is to make machine learning (ML) more accessible. Through many conversations over the past years, I learned about barriers that many ML beginners face. Existing ML environments are often too complex for beginners, or too limited to support modern ML experimentation. Beginners want to quickly start learning and not worry about spinning up infrastructure, configuring services, or implementing billing alarms to avoid going over budget. This emphasizes another barrier for many people: the need to provide billing and credit card information at sign-up. What if you could have a predictable and controlled environment for hosting your Jupyter notebooks in which you can’t accidentally run up a big bill? One that doesn’t require billing and credit card…
Tag: AWS News Blog
Announcing Amazon SageMaker Inference Recommender
Today, we’re pleased to announce Amazon SageMaker Inference Recommender — a brand-new Amazon SageMaker Studio capability that automates load testing and optimizes model performance across machine learning (ML) instances. Ultimately, it reduces the time it takes to get ML models from development to production and optimizes the costs associated with their operation. Until now, no service has provided MLOps Engineers with a means to pick the optimal ML instances for their model. To optimize costs and maximize instance utilization, MLOps Engineers would have to use their experience and intuition to select an ML instance type that would serve them and their model well, given the requirements to run them. Moreover, given the vast array of ML instances available, and the…
New – Introducing SageMaker Training Compiler
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…
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…