Favorite Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. Amazon SageMaker Pipelines enables you to build a secure, scalable, and flexible MLOps platform within Studio. In this post, we explain how to run PySpark processing jobs within

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Shared by AWS Machine Learning April 11, 2023

Favorite Enterprise customers have multiple lines of businesses (LOBs) and groups and teams within them. These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. These enterprise customers that are starting to

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Shared by AWS Machine Learning April 11, 2023

Favorite Developing web interfaces to interact with a machine learning (ML) model is a tedious task. With Streamlit, developing demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. As a

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Shared by AWS Machine Learning April 11, 2023

Favorite Financial services, the gig economy, telco, healthcare, social networking, and other customers use face verification during online onboarding, step-up authentication, age-based access restriction, and bot detection. These customers verify user identity by matching the user’s face in a selfie captured by a device camera with a government-issued identity card

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Shared by AWS Machine Learning April 11, 2023

Favorite You don’t have to be an expert in machine learning (ML) to appreciate the value of large language models (LLMs). Better search results, image recognition for the visually impaired, creating novel designs from text, and intelligent chatbots are just some examples of how these models are facilitating various applications

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Shared by AWS Machine Learning April 10, 2023

Favorite In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models using Amazon SageMaker JumpStart. Today, we are excited to introduce a new feature that enables users to inpaint images with Stable Diffusion models. Inpainting refers to the process of replacing a portion

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Shared by AWS Machine Learning April 10, 2023

Favorite With the advent of high-speed 5G mobile networks, enterprises are more easily positioned than ever with the opportunity to harness the convergence of telecommunications networks and the cloud. As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy

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Shared by AWS Machine Learning April 7, 2023

Favorite This is a joint post by NXP SEMICONDUCTORS N.V. & AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. In this post, we demonstrate how NXP, an

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Shared by AWS Machine Learning April 6, 2023

Favorite Data is at the heart of machine learning (ML). Including relevant data to comprehensively represent your business problem ensures that you effectively capture trends and relationships so that you can derive the insights needed to drive business decisions. With Amazon SageMaker Canvas, you can now import data from over

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Shared by AWS Machine Learning April 6, 2023

Favorite The size and complexity of large language models (LLMs) have exploded in the last few years. LLMs have demonstrated remarkable capabilities in learning the semantics of natural language and producing human-like responses. Many recent LLMs are fine-tuned with a powerful technique called instruction tuning, which helps the model perform

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Shared by AWS Machine Learning April 5, 2023