Category: Amazon SageMaker

Proteins drive many biological processes, such as enzyme activity, molecular transport, and cellular support. The three-dimensional structure of a protein provides insight into its function and how it interacts with other biomolecules. Experimental methods to determine protein structure, such as X-ray crystallography and NMR spectroscopy, are expensive and time-consuming. In contrast, recently-developed computational methods can […]

Healthcare data is complex and siloed, and exists in various formats. An estimated 80% of data within organizations is considered to be unstructured or “dark” data that is locked inside text, emails, PDFs, and scanned documents. This data is difficult to interpret or analyze programmatically and limits how organizations can derive insights from it and […]

Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. Of these options, one of the key features that SageMaker provides is real-time inference. Real-time inference workloads can have varying levels of requirements and service level agreements (SLAs) in terms of latency and […]

June 2024: The contents of this post are out of date. We recommend you refer to Announcing the general availability of fully managed MLflow on Amazon SageMaker for the latest. With Amazon SageMaker, you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such […]

Sometimes it can be very beneficial to use tools such as compilers that can modify and compile your models for optimal inference performance. In this post, we explore TensorRT and how to use it with Amazon SageMaker inference using NVIDIA Triton Inference Server. We explore how TensorRT works and how to host and optimize these […]

This is a joint post co-written by AWS and Voxel51. Voxel51 is the company behind FiftyOne, the open-source toolkit for building high-quality datasets and computer vision models. A retail company is building a mobile app to help customers buy clothes. To create this app, they need a high-quality dataset containing clothing images, labeled with different […]

The world of artificial intelligence (AI) and machine learning (ML) has been witnessing a paradigm shift with the rise of generative AI models that can create human-like text, images, code, and audio. Compared to classical ML models, generative AI models are significantly bigger and more complex. However, their increasing complexity also comes with high costs […]

Support for AWS DeepComposer will be ending soon. Please see Support for AWS DeepComposer ending soon for more details. Generative AI is gaining a lot of public attention at present, with talk around products such as GPT4, ChatGPT, DALL-E2, Bard, and many other AI technologies. Many customers have been asking for more information on AWS’s […]

New generations of CPUs offer a significant performance improvement in machine learning (ML) inference due to specialized built-in instructions. Combined with their flexibility, high speed of development, and low operating cost, these general-purpose processors offer an alternative to other existing hardware solutions. AWS, Arm, Meta and others helped optimize the performance of PyTorch 2.0 inference […]

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for ML. It provides a single, web-based visual interface where you can perform all machine learning (ML) development steps required to build, train, tune, debug, deploy, and monitor models. It gives data scientists all the tools you need to take ML models from experimentation […]