AWS Machine Learning Blog

OCTOBER 6, 2023

Large language models (LLMs) are revolutionizing fields like search engines, natural language processing (NLP), healthcare, robotics, and code generation. For ingestion, data can be updated in an offline mode, whereas inference needs to happen in milliseconds.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

An intelligent document processing (IDP) project typically combines optical character recognition (OCR) and natural language processing (NLP) to automatically read and understand documents. Effectively manage your data and its lifecycle Data plays a key role throughout your IDP solution.

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AWS Machine Learning Blog

MARCH 1, 2023

Solution overview Amazon Comprehend is a fully managed service that uses natural language processing (NLP) to extract insights about the content of documents. An Amazon Comprehend flywheel automates this ML process, from data ingestion to deploying the model in production.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific terms or words. His focus is natural language processing and computer vision.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

An IDP pipeline usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific terms or words. Keep documentation of processing rules thorough and up to date, fostering a transparent environment for all stakeholders.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Additionally, the solution must handle high data volumes with low latency and high throughput. This includes data ingestion, data preprocessing, converting documents to document types accepted by Amazon Textract, handling incoming document streams, routing documents by type, and implementing access control and retention policies.

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Pickl AI

DECEMBER 26, 2024

Semantic search uses Natural Language Processing (NLP) and Machine Learning to interpret the intent behind a users query, enabling more accurate and contextually relevant results. Traditional keyword-based search relies on exact term matches.

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Pragnakalp

JULY 13, 2023

LlamaIndex is an impressive data framework designed to support the development of applications utilizing LLMs (Large Language Models). It offers a wide range of essential tools that simplify tasks such as data ingestion, organization, retrieval, and integration with different application frameworks.

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Topbots

FEBRUARY 19, 2024

Networking Capabilities: Ensure your infrastructure has the networking capabilities to handle large volumes of data transfer. Data Pipeline Management: Set up efficient data pipelines for data ingestion, processing, and management.

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ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The benchmark used is the RoBERTa-Base, a popular model used in natural language processing (NLP) applications, that uses the transformer architecture. The automated process of data ingestion, processing, packaging, combination, and prediction is referred to by WorldQuant as their “alpha factory.”

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The MLOps Blog

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., The next section delves into these architectural patterns, exploring how they are leveraged in machine learning pipelines to streamline data ingestion, processing, model training, and deployment.

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Unite.AI

NOVEMBER 11, 2024

By leveraging ML and natural language processing (NLP) techniques, CRM platforms can collect raw data from disparate sources, such as purchase patterns, customer interactions, buying behavior, and purchasing history. Continuous Improvement AI-based CRMs can handle large amounts of data continuously.

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AWS Machine Learning Blog

AUGUST 20, 2024

The inherent ambiguity of natural language can also result in multiple interpretations of a single query, making it difficult to accurately understand the user’s precise intent. To bridge this gap, you need advanced natural language processing (NLP) to map user queries to database schema, tables, and operations.

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Bugra Akyildiz

FEBRUARY 24, 2024

Consider these technologies: Content-based filtering techniques: Utilizing natural language processing (NLP) techniques like word embeddings and topic modeling (e.g., Distributed computing platforms: Spark and Ray enable parallel processing and model training on large datasets,crucial for real-time scalability.

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The MLOps Blog

MARCH 21, 2023

Machine learning platform in healthcare There are mostly three areas of ML opportunities for healthcare, including computer vision, predictive analytics, and natural language processing. The most important requirement you need to incorporate into your platform for this vertical is the regulation of data and algorithms.

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Flipboard

NOVEMBER 26, 2024

Implement the solution The following illustrates the solution architecture: Architecture Diagram for Custom Hallucination Detection and Mitigation The overall workflow involves the following steps: Data ingestion involving raw PDFs stored in an Amazon Simple Storage Service (Amazon S3) bucket synced as a data source with.

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ODSC - Open Data Science

FEBRUARY 24, 2025

Relational databases like Postgres and Oracle were effective for structured data but required technical proficiency. Search tools like Elastic Search and Solr offered robust solutions for querying unstructured information, but Natural Language Processing (NLP) techniques such as TF-IDF and BM25 often lacked contextual understanding.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Data lineage and auditing – Metadata can provide information about the provenance and lineage of documents, such as the source system, data ingestion pipeline, or other transformations applied to the data. This information can be valuable for data governance, auditing, and compliance purposes.

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