AWS Machine Learning Blog

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 […]

Developers, to help you advance your AI and machine learning (ML) skills with hands-on and engaging learning, the AWS Machine Learning Scholarship Program from Udacity is now open for enrollment. AWS and Udacity are collaborating to educate developers of all skill levels to expand their AWS ML expertise. In this scholarship program, all eligible students […]

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 […]

As the world responds to the ongoing pandemic, it’s more important than ever to accurately access, consume, and analyze information related to COVID-19. Topics about the healthcare crisis permeate many dimensions of our personal and professional lives, through channels as diverse as news reporting, social media, business meetings, radio and podcasts, customer support calls, and […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Veeva Systems is a provider of cloud-based software for the global life sciences industry, which offers products that serve multiple domains ranging from clinical, regulatory, quality, and more. Veeva’s Vault Platform manages both content and data in a single platform […]

As the touchpoints customers use to engage with brands move to an increasingly complex mixture of digital and real-life interactions, you’re faced with the daunting task of delighting your customers with personalized experiences that hit the mark across these channels. Customer expectations are evolving as well. Today’s customers quickly lose patience with brands that can’t […]

Tens of thousands of customers rely on AWS for building machine learning (ML) applications. Customers like Airbnb and Pinterest use AWS to optimize their search recommendations, Lyft and Toyota Research Institute to develop their autonomous vehicle programs, and Capital One and Intuit to build and deploy AI-powered customer assistants. AWS offers the broadest and deepest […]