ML @ CMU

MARCH 31, 2023

Our work further motivates novel directions for developing and evaluating tools to support human-ML interactions. Model explanations have been touted as crucial information to facilitate human-ML interactions in many real-world applications where end users make decisions informed by ML predictions.

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Data Science Dojo

MARCH 24, 2023

Look no further than ML Ops – the future of ML deployment. Machine Learning (ML) has become an increasingly valuable tool for businesses and organizations to gain insights and make data-driven decisions. However, deploying and maintaining ML models can be a complex and time-consuming process. What is ML Ops?

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Flipboard

FEBRUARY 11, 2025

Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management. These tasks often involve processing vast amounts of documents, which can be time-consuming and labor-intensive. This solution uses the powerful capabilities of Amazon Q Business.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

In this post, we focus on one such complex workflow: document processing. Rule-based systems or specialized machine learning (ML) models often struggle with the variability of real-world documents, especially when dealing with semi-structured and unstructured data.

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Flipboard

JANUARY 6, 2025

The platform helped the agency digitize and process forms, pictures, and other documents. The federal government agency Precise worked with needed to automate manual processes for document intake and image processing. The demand for modernization is growing, and Precise can help government agencies adopt AI/ML technologies.

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AWS Machine Learning Blog

DECEMBER 12, 2024

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. For the detailed list of pre-set values, refer to the SDK documentation.

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Flipboard

NOVEMBER 19, 2024

Organizations possess extensive repositories of digital documents and data that may remain underutilized due to their unstructured and dispersed nature. Information repository – This repository holds essential documents and data that support customer service processes.

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AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. An effective approach that addresses a wide range of observed issues is the establishment of an AI/ML center of excellence (CoE). What is an AI/ML CoE?

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Flipboard

NOVEMBER 19, 2024

A common adoption pattern is to introduce document search tools to internal teams, especially advanced document searches based on semantic search. In a real-world scenario, organizations want to make sure their users access only documents they are entitled to access. The following diagram depicts the solution architecture.

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Machine Learning Research at Apple

FEBRUARY 6, 2023

Lack of diversity in data collection has caused significant failures in machine learning (ML) applications. While ML developers perform post-collection interventions, these are time intensive and rarely comprehensive. Our process includes (1) Pre-Collection Planning, to reflexively prompt and document…

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Flipboard

NOVEMBER 15, 2024

By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment. The documentation can be in a variety of formats.

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AWS Machine Learning Blog

APRIL 11, 2024

Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and extract information remains expensive, error prone, and difficult to scale. Categorizing documents is an important first step in IDP systems.

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AWS Machine Learning Blog

FEBRUARY 7, 2024

In the first post of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. The following diagram represents each stage in a mortgage document fraud detection pipeline.

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Flipboard

OCTOBER 25, 2023

Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data.

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AWS Machine Learning Blog

MARCH 21, 2025

Research papers and engineering documents often contain a wealth of information in the form of mathematical formulas, charts, and graphs. Navigating these unstructured documents to find relevant information can be a tedious and time-consuming task, especially when dealing with large volumes of data.

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Dataconomy

MAY 22, 2023

It is recommended to evaluate each framework’s documentation, performance benchmarks, and community support to determine the best fit for your distributed learning needs. The choice of framework depends on specific project requirements, existing infrastructure, and familiarity with the framework’s APIs and community resources.

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Flipboard

MARCH 21, 2025

AI for IT operations (AIOps) is the application of AI and machine learning (ML) technologies to automate and enhance IT operations. They are commonly used to document repetitive tasks, troubleshooting steps, and routine maintenance.

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Dataconomy

MARCH 12, 2025

As AI technologies continue to evolve and impact various sectors, the need for clear, standardized documentation about machine learning models grows ever more critical. Model cards serve as documentation tools that provide comprehensive insights into machine learning models. What are model cards?

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Data Science Dojo

JANUARY 22, 2025

Heres how embeddings power these advanced systems: Semantic Understanding LLMs use embeddings to represent words, sentences, and entire documents in a way that captures their semantic meaning. The process enables the models to find the most relevant sections of a document or dataset, improving the accuracy and relevance of their outputs.

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AWS Machine Learning Blog

AUGUST 9, 2024

Question and answering (Q&A) using documents is a commonly used application in various use cases like customer support chatbots, legal research assistants, and healthcare advisors. In this collaboration, the AWS GenAIIC team created a RAG-based solution for Deltek to enable Q&A on single and multiple government solicitation documents.

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AWS Machine Learning Blog

AUGUST 14, 2024

While using their data source, they want better visibility into the document processing lifecycle during data source sync jobs. They want to know the status of each document they attempted to crawl and index, as well as the ability to troubleshoot why certain documents were not returned with the expected answers.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

You can try out the models with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. To learn more, refer to the API documentation. Both models support a context window of 32,000 tokens, which is roughly 50 pages of text.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

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AWS Machine Learning Blog

APRIL 26, 2024

Today, we’re introducing the new capability to chat with your document with zero setup in Knowledge Bases for Amazon Bedrock. With this new capability, you can securely ask questions on single documents, without the overhead of setting up a vector database or ingesting data, making it effortless for businesses to use their enterprise data.

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Flipboard

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. Deploy traditional models to SageMaker endpoints In the following examples, we showcase how to use ModelBuilder to deploy traditional ML models.

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Data Science Dojo

MARCH 24, 2023

Look no further than MLOps – the future of ML deployment. Machine Learning (ML) has become an increasingly valuable tool for businesses and organizations to gain insights and make data-driven decisions. However, deploying and maintaining ML models can be a complex and time-consuming process.

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Hacker News

DECEMBER 20, 2023

Generative Machine Learning models have been well documented as being able to produce explicit adult content, including child sexual abuse material (CSAM) as well as to alter benign imagery of a cl.

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AWS Machine Learning Blog

OCTOBER 14, 2024

For many of these use cases, businesses are building Retrieval Augmented Generation (RAG) style chat-based assistants, where a powerful LLM can reference company-specific documents to answer questions relevant to a particular business or use case. Generate a grounded response to the original question based on the retrieved documents.

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AWS Machine Learning Blog

OCTOBER 31, 2024

Google Drive supports storing documents such as Emails contain a wealth of information found in different places, such as within the subject of an email, the message content, or even attachments. Types of documents Gmail messages can be sorted and stored inside your email inbox using folders and labels.

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AWS Machine Learning Blog

MAY 1, 2024

This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. For documentation retrieval, Retrieval Augmented Generation (RAG) stands out as a key tool. Virginia) AWS Region. The following diagram illustrates the solution architecture.

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

This allows SageMaker Studio users to perform petabyte-scale interactive data preparation, exploration, and machine learning (ML) directly within their familiar Studio notebooks, without the need to manage the underlying compute infrastructure. This same interface is also used for provisioning EMR clusters. python3.11-pip jars/livy-repl_2.12-0.7.1-incubating.jar

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AWS Machine Learning Blog

NOVEMBER 15, 2024

This significant improvement showcases how the fine-tuning process can equip these powerful multimodal AI systems with specialized skills for excelling at understanding and answering natural language questions about complex, document-based visual information. For a detailed walkthrough on fine-tuning the Meta Llama 3.2

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AWS Machine Learning Blog

OCTOBER 10, 2024

Amazon Lookout for Vision , the AWS service designed to create customized artificial intelligence and machine learning (AI/ML) computer vision models for automated quality inspection, will be discontinuing on October 31, 2025.

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AWS Machine Learning Blog

MARCH 21, 2025

See Best practices for data source connector configuration in Amazon Q Business to understand best practices To improve retrieved results and customize the end user chat experience, use Amazon Q to map document attributes from your data sources to fields in your Amazon Q index. Select the data source to edit its configurations under Actions.

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AWS Machine Learning Blog

AUGUST 21, 2024

Today, physicians spend about 49% of their workday documenting clinical visits, which impacts physician productivity and patient care. By using the solution, clinicians don’t need to spend additional hours documenting patient encounters. This blog post focuses on the Amazon Transcribe LMA solution for the healthcare domain.

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Flipboard

JANUARY 14, 2025

The documents uploaded to the knowledge base on the rack might be private and sensitive documents, so they wont be transferred to the AWS Region and will remain completely local on the Outpost rack. This vector database will store the vector representations of your documents, serving as a key component of your local Knowledge Base.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Challenges in deploying advanced ML models in healthcare Rad AI, being an AI-first company, integrates machine learning (ML) models across various functions—from product development to customer success, from novel research to internal applications. Rad AI’s ML organization tackles this challenge on two fronts.

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