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Traditional vs Vector databases: Your guide to make the right choice

Data Science Dojo

This blog delves into a detailed comparison between the two data management techniques. Hence, this blog will explore the debate from a few particular aspects, highlighting the characteristics of both traditional and vector databases in the process. A file records vectors that belong to each cluster.

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How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations

AWS Machine Learning Blog

Smart Subgroups For a user-specified patient population, the Smart Subgroups feature identifies clusters of patients with similar characteristics (for example, similar prevalence profiles of diagnoses, procedures, and therapies). The cluster feature summaries are stored in Amazon S3 and displayed as a heat map to the user.

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Monitoring of Jobskills with Data Engineering & AI

Data Science Blog

The data is obtained from the Internet via APIs and web scraping, and the job titles and the skills listed in them are identified and extracted from them using Natural Language Processing (NLP) or more specific from Named-Entity Recognition (NER).

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An Introduction to Natural Language Processing (NLP)

Pickl AI

Well, it’s Natural Language Processing which equips the machines to work like a human. But there is much more to NLP, and in this blog, we are going to dig deeper into the key aspects of NLP, the benefits of NLP and Natural Language Processing examples. What is NLP?

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How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model

AWS Machine Learning Blog

During the training process, our SageMaker HyperPod cluster was connected to this S3 bucket, enabling effortless retrieval of the dataset elements as needed. The deduplication process involved embedding dataset elements using a text embedder, then computing cosine similarity between the embeddings to identify similar elements.

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Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

AWS Machine Learning Blog

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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Train, optimize, and deploy models on edge devices using Amazon SageMaker and Qualcomm AI Hub

AWS Machine Learning Blog

Business challenge Today, many developers use AI and machine learning (ML) models to tackle a variety of business cases, from smart identification and natural language processing (NLP) to AI assistants. After the training is complete, SageMaker spins down the cluster, and you’re billed for the net training time in seconds.

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