AWS Machine Learning Blog

APRIL 16, 2024

In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.

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Dataconomy

MARCH 17, 2025

This capability, rooted in the sophisticated world of Natural Language Processing (NLP), removes the barriers that often complicate data retrieval and analysis, making insights accessible to everyone, regardless of their technical expertise. What is Natural Language Query (NLQ)?

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. With the emergence of large language models (LLMs), NLP-based SQL generation has undergone a significant transformation.

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AWS Machine Learning Blog

JANUARY 4, 2024

One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing with complex technical code, business users and data analysts can ask questions related to data and insights in plain language.

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AWS Machine Learning Blog

DECEMBER 6, 2023

Overview of RAG RAG solutions are inspired by representation learning and semantic search ideas that have been gradually adopted in ranking problems (for example, recommendation and search) and natural language processing (NLP) tasks since 2010.

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Data Science Dojo

MAY 18, 2023

Deep Learning with KNIME: This tutorial will provide theoretical and practical introductions to three deep learning topics using the KNIME Analytics Platform’s Keras Integration; first, how to configure and train an LSTM network for language generation; we’ll have some fun with this and generate fresh rap songs!

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Flipboard

DECEMBER 6, 2023

In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution. RAG with semantic search – Conventional RAG with semantic search was the last step before moving to SQL generation.

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AWS Machine Learning Blog

AUGUST 30, 2024

In this post, we provide an overview of the Meta Llama 3 models available on AWS at the time of writing, and share best practices on developing Text-to-SQL use cases using Meta Llama 3 models. Meta Llama 3’s capabilities enhance accuracy and efficiency in understanding and generating SQL queries from natural language inputs.

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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. However, it can also be a time-consuming and computationally expensive process.

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AWS Machine Learning Blog

AUGUST 20, 2024

Natural language is ambiguous and imprecise, whereas data adheres to rigid schemas. For example, SQL queries can be complex and unintuitive for non-technical users. Handling complex queries involving multiple tables, joins, and aggregations makes it difficult to interpret user intent and translate it into correct SQL operations.

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AWS Machine Learning Blog

JUNE 13, 2023

The natural language capabilities allow non-technical users to query data through conversational English rather than complex SQL. The AI and language models must identify the appropriate data sources, generate effective SQL queries, and produce coherent responses with embedded results at scale.

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AWS Machine Learning Blog

DECEMBER 29, 2022

It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. The CloudFormation script created a database called sagemaker. Let’s populate this database with tables for the RStudio user to query. Loading data in Amazon Redshift Serverless.

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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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Data Science Dojo

JULY 3, 2024

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

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Mlearning.ai

JUNE 17, 2023

Photo by Sneaky Elbow on Unsplash The advent of large language models (LLMs), such as OpenAI’s GPT-3, has ushered in a new era of possibilities in the realm of natural language processing. At present, there’s a growing buzz around Vector Databases. However, these new technologies bring their own set of challenges.

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AWS Machine Learning Blog

APRIL 24, 2024

We use Knowledge Bases for Amazon Bedrock to fetch from historical data stored as embeddings in the Amazon OpenSearch Service vector database. An LLM evaluates each question along with the chat history from the same session to determine its nature and which subject area it falls under (such as SQL, action, search, or SME).

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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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Dataconomy

OCTOBER 15, 2024

Natural Language Processing (NLP) for application design One of the most significant intersections between Gen AI and low-code development is through NLP. Developers can interact with LCNC platforms using natural language queries or prompts. Gen AI plays a pivotal role in automating these processes.

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AWS Machine Learning Blog

MARCH 14, 2024

We formulated a text-to-SQL approach where by a user’s natural language query is converted to a SQL statement using an LLM. The SQL is run by Amazon Athena to return the relevant data. Amazon Kendra uses natural language processing (NLP) to understand user queries and find the most relevant documents.

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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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ODSC - Open Data Science

MARCH 3, 2025

Their work has set a gold standard for integrating advanced natural language processing (NLP ) into clinical settings. Speed: Handling large patient histories stretches GPT models, even with long context windows, and demands pre-optimized databases. Consistency: Variability in responses undermines clinician trust.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Our pipeline belongs to the general ETL (extract, transform, and load) process family that combines data from multiple sources into a large, central repository. Multiple days of data can be processed by separate Processing jobs simultaneously. Employ AWS Glue for data crawling after processing multiple days of data.

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Mlearning.ai

MAY 3, 2023

Vector Databases — Long Term Memory for AI Photo by Sven Brandsma on Unsplash Introduction In natural language processing (NLP), a vector is a mathematical representation of a word or text document. The Database One such solution for this problem is Elasticsearch. Conclusion So easy right!!

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AWS Machine Learning Blog

JUNE 25, 2024

Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. In addition, the generative business intelligence (BI) capabilities of QuickSight allow you to ask questions about customer feedback using natural language, without the need to write SQL queries or learn a BI tool.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

For structured data, the agent uses the SQL Connector and SQLAlchemy to analyze databases, which includes Amazon Athena. Session(region_name=region_name) athena_client = session.client('athena') database=database_name table=table_Name. It can query a stocks database to answer questions on stocks.

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AWS Machine Learning Blog

JUNE 26, 2024

Analysts need to learn new tools and even some programming languages such as SQL (with different variations). Action groups – Action groups are interfaces that an agent uses to interact with the different underlying components such as APIs and databases.

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ODSC - Open Data Science

FEBRUARY 14, 2023

These models are the technology behind Open AI’s DALL-E and GPT-3 , and are powerful enough to understand natural language commands and generate high-quality code to instantly query databases. They can be fine-tuned on a smaller dataset to perform a specific task, such as language translation or summarization.

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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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AWS Machine Learning Blog

NOVEMBER 27, 2023

This could involve better preprocessing tools, semi-supervised learning techniques, and advances in natural language processing. Access to Amazon OpenSearch as a vector database. The choice of vector database is an important architectural decision. Clean data is important for good model performance.

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AWS Machine Learning Blog

AUGUST 12, 2024

These could include exports from customer relationship management (CRM), configuration management database (CMDB), and electronic health record (EHR) systems. Analytics tooling such as Amazon Athena can now be used to query the data using SQL. The approach is shown in the following diagram.

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phData

SEPTEMBER 11, 2024

With Cortex Analyst from the Snowflake AI Data Cloud , business users can transform plain English questions into SQL queries, enabling self-service analytics and making data insights more accessible. This is especially powerful for users who may have pressing questions about their data but might not have the SQL-writing experience to do so.

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Mlearning.ai

JUNE 23, 2023

A database that help index and search at blazing speed. Relational databases (like MySQL) or No-SQL databases (AWS DynamoDB) can store structured or even semi-structured data but there is one inherent problem. Unstructured data is hard to store in relational databases.

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Pickl AI

JULY 18, 2023

Additionally, its natural language processing capabilities and Machine Learning frameworks like TensorFlow and scikit-learn make Python an all-in-one language for Data Science. SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases.

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The MLOps Blog

MAY 10, 2023

To ensure security and JSON/pickle benefits, you can save your model to a dedicated database. Next, you will see how you can save an ML model in a database. Storing ML models in a database There is also scope for you to save your ML models in relational databases PostgreSQL , MySQL , Oracle SQL , etc.

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ODSC - Open Data Science

FEBRUARY 5, 2025

The Evolving AI Development Lifecycle Despite the revolutionary capabilities of LLMs, the core development lifecycle established by traditional natural language processing remains essential: Plan, Prepare Data, Engineer Model, Evaluate, Deploy, Operate, and Monitor. Previously, consultants spent weeks manually querying data.

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ODSC - Open Data Science

MAY 11, 2023

Origins of Generative AI and Natural Language Processing with ChatGPT Joining in on the fun of using generative AI, we used ChatGPT to help us explore some of the key innovations over the past 50 years of AI. Databases for the Era of Artificial Intelligence Everyone is talking about ChatGPT.

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phData

MARCH 3, 2023

Whether you want nodes to publish your data to Tableau Server, connect to a Snowflake Data Cloud database , or perform image or audio analyses, there is an extension for you. If you need to connect to a database for any purpose, this extension cannot be ignored. These include Microsoft SQL Server, MySQL, Oracle, and PostgreSQL.

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