Data Science Dojo

AUGUST 18, 2023

Transformers are a type of neural network that are well-suited for natural language processing tasks. They are able to learn long-range dependencies between words, which is essential for understanding the nuances of human language. They are typically trained on clusters of computers or even on cloud computing platforms.

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Flipboard

DECEMBER 3, 2024

This NoSQL database is optimized for rapid access, making sure the knowledge base remains responsive and searchable. The agent uses natural language processing (NLP) to understand the query and uses underlying agronomy models to recommend optimal seed choices tailored to specific field conditions and agronomic needs.

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Dataconomy

SEPTEMBER 22, 2023

Databases are the unsung heroes of AI Furthermore, data archiving improves the performance of applications and databases. By removing infrequently accessed data from primary storage systems, organizations can improve the performance of their applications and databases, which can lead to increased productivity and efficiency.

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Clustering Algorithm Data Classification Machine Learning 185

Flipboard

MAY 16, 2025

One of the foundational services is Amazon Elastic Compute Cloud (EC2), which allows users to have at their disposal a virtual cluster of computers, with extremely high availability, which can be interacted with over the internet via REST APIs, a CLI or the AWS console. Assistant: 0.05 Context: Amazon Web Services, Inc.

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AWS Cloud Computing Natural Language Processing AI 107

AWS Machine Learning Blog

DECEMBER 6, 2023

In this blog post, we’ll explore how to deploy LLMs such as Llama-2 using Amazon Sagemaker JumpStart and keep our LLMs up to date with relevant information through Retrieval Augmented Generation (RAG) using the Pinecone vector database in order to prevent AI Hallucination. Sign up for a free-tier Pinecone Vector Database.

Database 131

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AWS Machine Learning Blog

DECEMBER 29, 2022

Note: If you already have an RStudio domain and Amazon Redshift cluster you can skip this step. Amazon Redshift Serverless cluster. There is no need to set up and manage clusters. The CloudFormation script created a database called sagemaker. Let’s populate this database with tables for the RStudio user to query.

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Pickl AI

MAY 10, 2023

ML algorithms fall into various categories which can be generally characterised as Regression, Clustering, and Classification. While Classification is an example of directed Machine Learning technique, Clustering is an unsupervised Machine Learning algorithm. It can also be used for determining the optimal number of clusters.

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Pickl AI

MARCH 13, 2023

The algorithm learns to find patterns or structure in the data by clustering similar data points together. WHAT IS CLUSTERING? Clustering is an unsupervised machine learning technique that is used to group similar entities. Those groups are referred to as clusters.

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AWS Machine Learning Blog

JUNE 11, 2024

The diverse and rich database of models brings unique challenges for choosing the most efficient deployment infrastructure that gives the best latency and performance. In our test environment, we observed 20% throughput improvement and 30% latency reduction across multiple natural language processing models.

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Machine Learning Machine Learning AWS Natural Language Processing 126

AWS Machine Learning Blog

JANUARY 31, 2024

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). Text embedding refers to the process of transforming text into numerical representations that reside in a high-dimensional vector space. The example matches a user’s query to the closest entries in an in-memory vector database.

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Natural Language Processing AWS Machine Learning Machine Learning 136

Towards AI

FEBRUARY 19, 2025

Vector Databases 101: A Beginners Guide to Vector Search and Indexing Photo by Google DeepMind on Unsplash Introduction Alright, folks! The secret sauce behind all of this is vector search and vector databases, helping power similarity-based recommendations and retrieval! Traditional databases? They tap out.

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Database K-nearest Neighbors Machine Learning Machine Learning 61

phData

DECEMBER 7, 2023

In our previous article on Retrieval Augmented Generation (RAG), we discussed the need for a Vector Database to retrieve additional information for our prompts. Today, we will dive into the inner workings of a Vector Database to better understand exactly how this technology functions. What is a Vector Database in Simple Terms?

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Database Natural Language Processing Clustering SQL 52

Hacker News

NOVEMBER 15, 2023

Patrick Lewis “We definitely would have put more thought into the name had we known our work would become so widespread,” Lewis said in an interview from Singapore, where he was sharing his ideas with a regional conference of database developers. “We Retrieval-augmented generation combines LLMs with embedding models and vector databases.

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AWS Machine Learning Blog

APRIL 17, 2023

Using RStudio on SageMaker and Amazon EMR together, you can continue to use the RStudio IDE for analysis and development, while using Amazon EMR managed clusters for larger data processing. In this post, we demonstrate how you can connect your RStudio on SageMaker domain with an EMR cluster. Choose Create stack.

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Clustering AWS Machine Learning Machine Learning 96

AWS Machine Learning Blog

SEPTEMBER 3, 2024

Cost optimization – The serverless nature of the integration means you only pay for the compute resources you use, rather than having to provision and maintain a persistent cluster. This same interface is also used for provisioning EMR clusters. The following diagram illustrates this solution.

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AWS Machine Learning Blog

FEBRUARY 2, 2024

Embeddings capture the information content in bodies of text, allowing natural language processing (NLP) models to work with language in a numeric form. In this pattern, the recipe text is converted into embedding vectors using an embedding model, and stored in a vector database.

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IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. K-means clustering is commonly used for market segmentation, document clustering, image segmentation and image compression.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

Sonnet model for natural language processing. Additionally, if a user tells the assistant something that should be remembered, we store this piece of information in a database and add it to the context every time the user initiates a request.

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AWS Machine Learning Blog

MARCH 10, 2025

It was built using a combination of in-house and external cloud services on Microsoft Azure for large language models (LLMs), Pinecone for vectorized databases, and Amazon Elastic Compute Cloud (Amazon EC2) for embeddings. Amazon Bedrock Guardrails implements content filtering and safety checks as part of the query processing pipeline.

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AWS Machine Learning Blog

APRIL 16, 2024

For example, you can visually explore data sources like databases, tables, and schemas directly from your JupyterLab ecosystem. After you have set up connections (illustrated in the next section), you can list data connections, browse databases and tables, and inspect schemas. This new feature enables you to perform various functions.

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SQL AWS Database Data Scientist 132

AWS Machine Learning Blog

AUGUST 8, 2024

By integrating LLMs, the WxAI team enables advanced capabilities such as intelligent virtual assistants, natural language processing (NLP), and sentiment analysis, allowing Webex Contact Center to provide more personalized and efficient customer support. The following diagram illustrates the WxAI architecture on AWS.

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Towards AI

MAY 3, 2023

Solving Machine Learning Tasks with MLCoPilot: Harnessing Human Expertise for Success Many of us have made use of large language models (LLMs) like ChatGPT to generate not only text and images but also code, including machine learning code. Vector databases can store them and are designed for search and data mining.

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ML ML Machine Learning Machine Learning 96

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

Data Science 150

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phData

NOVEMBER 28, 2024

“ Vector Databases are completely different from your cloud data warehouse.” – You might have heard that statement if you are involved in creating vector embeddings for your RAG-based Gen AI applications. Text splitting is breaking down a long document or text into smaller, manageable segments or “chunks” for processing.

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AWS Machine Learning Blog

JANUARY 12, 2024

This is an open source dataset curated for financial natural language processing (NLP) and is available on a GitHub repository. You don’t have to change to a vector database or make drastic changes to your infrastructure, and it only takes a few lines of code. Rerank is available in Amazon SageMaker.

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Natural Language Processing AWS Data Science Database 142

AWS Machine Learning Blog

JULY 11, 2024

RAG provides additional knowledge to the LLM through its input prompt space and its architecture typically consists of the following components: Indexing : Prepare a corpus of unstructured text, parse and chunk it, and then, embed each chunk and store it in a vector database.

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AWS Machine Learning Blog

JUNE 6, 2024

A RAG system uses a vector database to serve as a knowledge retriever. It must extract a query from a user’s prompt and send it to a vector database to reliably find as much semantic information as possible. A smaller vector size means a great amount of cost savings while storing them in a vector database.

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AWS Machine Learning Blog

JUNE 21, 2024

He has deep ML experience in speech recognition, translation, natural language processing, and advertising, and has published over 30 papers in these areas. Dustin Hillard is responsible for leading product development and technology innovation, systems teams, and corporate IT at eSentire.

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AWS Machine Learning Blog

AUGUST 30, 2024

The 8-billion-parameter model integrates grouped-query attention (GQA) for improved processing of longer data sequences, enhancing real-world application performance. Training involved a dataset of over 15 trillion tokens across two GPU clusters, significantly more than Meta Llama 2.

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AWS Machine Learning Blog

MARCH 19, 2024

Generative language models have proven remarkably skillful at solving logical and analytical natural language processing (NLP) tasks. DynamoDB table An application running on AWS uses an Amazon Aurora Multi-AZ DB cluster deployment for its database. Enable read-through caching on the Aurora database.

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Database AWS Python Natural Language Processing 138

How to Learn Machine Learning

MAY 2, 2025

Compute Resources : Azure ML provides scalable compute options like training clusters, inference clusters, and compute instances that can be automatically scaled based on workload demands. Text and NLP : Natural language processing tasks such as sentiment analysis, named entity recognition, and text classification are well-supported.

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Data Science 101

OCTOBER 31, 2019

The idea is to build computer programs that sift through databases automatically, seeking regularities or patterns. It is used to extract information from the raw data in databases… “ Overview. Clustering. For example, clustering is used to group a large set of documents into categories based on the content.

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Pickl AI

JULY 17, 2023

Unsupervised Learning Algorithms Unsupervised Learning Algorithms tend to perform more complex processing tasks in comparison to supervised learning. However, unsupervised learning can be highly unpredictable compared to natural learning methods. K-Means Clustering: K-means is a popular and widely used clustering algorithm.

Machine Learning 52

Machine Learning Machine Learning Clustering K-nearest Neighbors 52

Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. What is required to build an AI system?

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AWS Machine Learning Blog

APRIL 19, 2024

We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) 7b) model to generate text responses to user questions based on the most similar slide retrieved from the vector database. Claude 3 Sonnet is the next generation of state-of-the-art models from Anthropic.

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IBM Journey to AI blog

MARCH 21, 2024

Leveraging distributed storage and processing frameworks such as Apache Hadoop, Spark or Dask accelerates data ingestion, transformation and analysis. Additionally, using in-memory databases and caching mechanisms minimizes latency and improves data access speeds.

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Applied Data Science

AUGUST 2, 2021

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data.

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