Category: Learning Levels

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. However, there are scenarios in which data scientists may prefer to transition from interactive development on notebooks to batch jobs. Examples of such use cases include scaling up […]

Amazon SageMaker Serverless Inference allows you to serve model inference requests in real time without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. You can let AWS handle the undifferentiated heavy lifting of managing the underlying infrastructure and save costs in the process. A Serverless Inference endpoint spins up […]

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 […]

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June 2024: The contents of this post are out of date.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 […]

In this post, we discuss a machine learning (ML) solution for complex image searches using Amazon Kendra and Amazon Rekognition. Specifically, we use the example of architecture diagrams for complex images due to their incorporation of numerous different visual icons and text. With the internet, searching and obtaining an image has never been easier. Most […]

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 […]

Time series forecasting refers to the process of predicting future values of time series data (data that is collected at regular intervals over time). Simple methods for time series forecasting use historical values of the same variable whose future values need to be predicted, whereas more complex, machine learning (ML)-based methods use additional information, such […]

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 generative AI solutions. The aim of this post is to address those needs. This post provides an overview of […]