新規事業統括部の山本です。 AWS re:Invent 2021に参加し現地のセッションを受けてきたので、内容をレポートします。 今回は、キーノートで発表された新サービスの1つである、Amazon SageMasker […]…
Now in Preview – Amazon SageMaker Studio Lab, a Free Service to Learn and Experiment with ML
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 […]
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 […]
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 […]
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 […]
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 […]
機械学習はもはやエンジニアのものですらない。ノーコードで機械学習モデルが作れる「Sagemaker Canvas」が発表！ #reinvent
せーのでございます。たった今re:Invent2021初日のキーノートが終了したところです。 今年も初日から色々なサービスが出ました。このエントリではその中からビジネスアナリスト向けの機械学習サービス「Sagemaker […]…
SageMakerの現在の上限値を確認する方法について教えてください
困っていた内容 先日、SageMakerの上限緩和申請を行いましたが、 現在上限に設定されている値は、CLIやコンソールから確認できますか？ どう対応すればいいの? 現時点のSageMakerは、現在の上限値をCLIやコ […]…
Amazon Sagemaker での「Error for Training job image-classification-xxxx-xx-xx-xx-xx-xx-xxx: Failed. Reason: ClientError: rec file provides 2-dimensional label, but label_width is set to 3.」 エラーの回避方法
困っていた内容 イメージ分離にて GroundTruth にてラベリングしたマルチラベル（3 ラベル）で学習ジョブを実行しているのですが、以下のエラーが発生しました。 エラーの回避方法を教えてください。 どう対応すればい […]…
Announcing Fully Managed RStudio on Amazon SageMaker for Data Scientists
Two years ago, we introduced Amazon SageMaker Studio, the industry’s first fully integrated development environment (IDE) for machine learning (ML). Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10 times Many data scientists love the R project, an […]