AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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AWS Machine Learning Blog

SEPTEMBER 6, 2024

Understanding the intrinsic value of data network effects, Vidmob constructed a product and operational system architecture designed to be the industry’s most comprehensive RLHF solution for marketing creatives. The main aspects of the LLM prompt include: Client description – Background information about the client.

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AWS Machine Learning Blog

MAY 23, 2023

With organizations increasingly investing in machine learning (ML), ML adoption has become an integral part of business transformation strategies. However, implementing ML into production comes with various considerations, notably being able to navigate the world of AI safely, strategically, and responsibly.

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AWS Machine Learning Blog

MARCH 10, 2025

Investment professionals face the mounting challenge of processing vast amounts of data to make timely, informed decisions. This challenge is particularly acute in credit markets, where the complexity of information and the need for quick, accurate insights directly impacts investment outcomes.

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AWS Machine Learning Blog

APRIL 2, 2025

Ray promotes the same coding patterns for both a simple machine learning (ML) experiment and a scalable, resilient production application. Overview of Ray This section provides a high-level overview of the Ray tools and frameworks for AI/ML workloads. We primarily focus on ML training use cases.

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AWS Machine Learning Blog

MARCH 5, 2025

It requires checking many systems and teams, many of which might be failing, because theyre interdependent. Developers need to reason about the system architecture, form hypotheses, and follow the chain of components until they have located the one that is the culprit.

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AWS Machine Learning Blog

MARCH 13, 2025

In synchronous orchestration, just like in traditional process automation, a supervisor agent orchestrates the multi-agent collaboration, maintaining a high-level view of the entire process while actively directing the flow of information and tasks. Understanding how to implement this type of pattern will be explained later in this post.

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O'Reilly Media

NOVEMBER 19, 2024

What’s old becomes new again: Substitute the term “notebook” with “blackboard” and “graph-based agent” with “control shell” to return to the blackboard system architectures for AI from the 1970s–1980s. See the Hearsay-II project , BB1 , and lots of papers by Barbara Hayes-Roth and colleagues. Does GraphRAG improve results?

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AWS Machine Learning Blog

NOVEMBER 17, 2023

Amazon Rekognition Content Moderation , a capability of Amazon Rekognition , automates and streamlines image and video moderation workflows without requiring machine learning (ML) experience. This process involves the utilization of both ML and non-ML algorithms. In this section, we briefly introduce the system architecture.

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Towards AI

MARCH 1, 2023

The technology behind GitHub’s new code search This post provides a high-level explanation of the inner workings of GitHub’s new code search and offers a glimpse into the system architecture and technical underpinnings of the product. Zero-Shot Information Extraction […]

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AWS Machine Learning Blog

NOVEMBER 24, 2023

Creating engaging and informative product descriptions for a vast catalog is a monumental task, especially for global ecommerce platforms. The README file contains all the information you need to get started, from requirements to deployment guidelines. This solution is available in the AWS Solutions Library.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud. You can find more information on the p4de.24xlarge

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Google Research AI blog

FEBRUARY 14, 2023

One of the underlying concepts is using LLMs to prompt other pretrained models for information that can build context about what is happening in a scene and make predictions about multimodal tasks. This is similar to the socratic method in teaching, where a teacher asks students questions to lead them through a rational thought process.

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AWS Machine Learning Blog

JANUARY 30, 2023

Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts, without requiring any prior ML experience. With Forecast, there are no servers to provision or ML models to build manually. For more information, refer to Training Predictors. Choose Create.

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Flipboard

DECEMBER 6, 2023

To meet the growing demand for efficient and dynamic data retrieval, Q4 aimed to create a chatbot Q&A tool that would provide an intuitive and straightforward method for IROs to access the necessary information they need in a user-friendly format. Financial markets is a regulated industry with high stakes involved.

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AWS Machine Learning Blog

FEBRUARY 19, 2025

Rather than using probabilistic approaches such as traditional machine learning (ML), Automated Reasoning tools rely on mathematical logic to definitively verify compliance with policies and provide certainty (under given assumptions) about what a system will or wont do. For more information, refer to Create a guardrail.

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Heartbeat

JANUARY 9, 2024

" The LLMOps Steps LLMs, sophisticated artificial intelligence (AI) systems trained on enormous text and code datasets, have changed the game in various fields, from natural language processing to content generation. Deployment : The adapted LLM is integrated into this stage's planned application or system architecture.

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IBM Journey to AI blog

JULY 18, 2024

In larger distributed systems whose components are separated by geography, components are connected through wide area networks (WAN). The components in a distributed system share information through an elaborate system of message-passing, over whichever type of network is being used. Distributed computing supplies both.

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The MLOps Blog

APRIL 17, 2023

As an MLOps engineer on your team, you are often tasked with improving the workflow of your data scientists by adding capabilities to your ML platform or by building standalone tools for them to use. Giving your data scientists a platform to track the progress of their ML projects. Experiment tracking is one such capability.

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Pickl AI

DECEMBER 10, 2024

The systems architecture combines Oracles hardware expertise with software optimisation to deliver unmatched performance. Providing instantaneous access to data insights empowers leaders to make informed choices without delays. This allows enterprises to store more information without expanding physical infrastructure.

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AWS Machine Learning Blog

AUGUST 15, 2023

Customers are increasingly turning to product reviews to make informed decisions in their shopping journey, whether they’re purchasing everyday items like a kitchen towel or making major purchases like buying a car. Amazon has one of the largest stores with hundreds of millions of items available.

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Flipboard

JANUARY 15, 2025

Today, teams at AWS operate a number of safety systems that maintain a high operational readiness bar, and work relentlessly on improving safety mechanisms and risk assessment processes. We conduct a rigorous planning process on a recurring basis to inform how we design and build our network, and maintain resiliency under various scenarios.

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AWS Machine Learning Blog

NOVEMBER 12, 2024

Focused on addressing the challenge of agricultural data standardization, Agmatix has developed proprietary patented technology to harmonize and standardize data, facilitating informed decision-making in agriculture. The transformed data acts as the input to AI/ML services.

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AWS Machine Learning Blog

JANUARY 28, 2025

Awareness of this variation can help you better interpret latency differences between models and make more informed decisions when selecting models for your applications. By understanding these nuances, you can make more informed decisions to optimize your AI applications for better user experience.

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