Category: Amazon SageMaker

Amazon SageMaker is a fully managed service that allows you to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. In August 2019, Amazon SageMaker announced the availability of the pre-installed R kernel in […]

Many AWS customers already use the popular open-source statistical computing and graphics software environment R for big data analytics and data science. Amazon SageMaker is a fully managed service that lets you build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to […]

Tabular data is a primary method to store data across multiple industries, including financial, healthcare, manufacturing, and many more. A large number of machine learning (ML) use cases deal with traditional structured or tabular data. For example, a fraud detection use case might be tabular inputs like a customer’s account history or payment details to […]

Today we’re announcing Amazon SageMaker Components for Kubeflow Pipelines. This post shows how to build your first Kubeflow pipeline with Amazon SageMaker components using the Kubeflow Pipelines SDK. Kubeflow is a popular open-source machine learning (ML) toolkit for Kubernetes users who want to build custom ML pipelines.  Kubeflow Pipelines is an add-on to Kubeflow that lets […]

Preferred Networks (PFN) released the first major version of their open-source hyperparameter optimization (HPO) framework Optuna in January 2020, which has an eager API. This post introduces a method for HPO using Optuna and its reference architecture in Amazon SageMaker. Amazon SageMaker supports various frameworks and interfaces such as TensorFlow, Apache MXNet, PyTorch, scikit-learn, Horovod, Keras, […]

At re:Invent 2019, AWS shared the fastest training times on the cloud for two popular machine learning (ML) models: BERT (natural language processing) and Mask-RCNN (object detection). To train BERT in 1 hour, we efficiently scaled out to 2,048 NVIDIA V100 GPUs by improving the underlying infrastructure, network, and ML framework. Today, we’re open-sourcing the optimized training code for […]

Managing the complete lifecycle of a deep learning project can be challenging, especially if you use multiple separate tools and services. For example, you may use different tools for data preprocessing, prototyping training and inference code, full-scale model training and tuning, model deployments, and workflow automation to orchestrate all of the above for production. Friction […]

In recent years, AWS customers have been running machine learning (ML) on an increasing variety of datasets and data sources. Because a large percentage of organizational data is stored in relational databases such as Amazon Aurora, there’s a common need to make this relational data available for training ML models, and to use ML models […]

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, […]