Category: Technical How-to

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of virtually infinite compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are rapidly adopting and using ML technologies to transform their businesses. Just recently, generative AI applications have […]

Generative AI is in the midst of a period of stunning growth. Increasingly capable foundation models are being released continuously, with large language models (LLMs) being one of the most visible model classes. LLMs are models composed of billions of parameters trained on extensive corpora of text, up to hundreds of billions or even a […]

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. Valuable data in organizations is stored in both structured and unstructured repositories. Amazon Kendra can pull together […]

AWS delivers services that meet customers’ artificial intelligence (AI) and machine learning (ML) needs with services ranging from custom hardware like AWS Trainium and AWS Inferentia to generative AI foundation models (FMs) on Amazon Bedrock. In February 2022, AWS and Hugging Face announced a collaboration to make generative AI more accessible and cost efficient. Generative […]

February 2024: This post was reviewed and updated for accuracy. This post is co-written with Thatcher Thornberry from bpx energy.  Facies classification is the process of segmenting lithologic formations from geologic data at the wellbore location. During drilling, wireline logs are obtained, which have depth-dependent geologic information. Geologists are deployed to analyze this log data […]

Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your ML inference needs. It’s a fully-managed service and integrates with MLOps tools so you can work to scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. SageMaker provides […]

April 2024: This post was reviewed and updated for accuracy. Understanding business trends, customer behavior, sales revenue, increase in demand, and buyer propensity all start with data. Exploring, analyzing, interpreting, and finding trends in data is essential for businesses to achieve successful outcomes. Business analysts play a pivotal role in facilitating data-driven business decisions through […]

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 […]