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Text analytics: Text analytics, also known as text mining, deals with unstructured text data, such as customer reviews, social media comments, or documents. It uses naturallanguageprocessing (NLP) techniques to extract valuable insights from textual data.
He specializes in large language models, cloud infrastructure, and scalable data systems, focusing on building intelligent solutions that enhance automation and data accessibility across Amazons operations.
The following is a high-level architecture of the solution we can build to process the unstructured data, assuming the input data is being ingested to the raw input object store. The steps of the workflow are as follows: Integrated AI services extract data from the unstructured data.
As organisations grapple with this vast amount of information, understanding the main components of BigData becomes essential for leveraging its potential effectively. Key Takeaways BigData originates from diverse sources, including IoT and social media.
As organisations grapple with this vast amount of information, understanding the main components of BigData becomes essential for leveraging its potential effectively. Key Takeaways BigData originates from diverse sources, including IoT and social media.
Social media conversations, comments, customer reviews, and image data are unstructured in nature and hold valuable insights, many of which are still being uncovered through advanced techniques like NaturalLanguageProcessing (NLP) and machine learning. Tools like Unstructured.io
Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure DataLake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.
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