Data Science Dojo

MARCH 27, 2025

It powers business decisions, drives AI models, and keeps databases running efficiently. Without proper organization, databases become bloated, slow, and unreliable. Essentially, data normalization is a database design technique that structures data efficiently. Think about itdata is everywhere.

Database 195

Database Data Warehouse Machine Learning Machine Learning 195

Dataconomy

AUGUST 7, 2023

Artificial intelligence is no longer fiction and the role of AI databases has emerged as a cornerstone in driving innovation and progress. An AI database is not merely a repository of information but a dynamic and specialized system meticulously crafted to cater to the intricate demands of AI and ML applications.

Database 168

Database AI AI ML 168

AWS Machine Learning Blog

MARCH 18, 2025

SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. This can be overwhelming for nontechnical users who lack proficiency in SQL. This application allows users to ask questions in natural language and then generates a SQL query for the users request.

SQL 113

SQL Database AI AI 113

Flipboard

APRIL 28, 2025

In this post, we employ the LLM-as-a-judge technique to evaluate the text-to-SQL and chain-of-thought capabilities of Amazon Bedrock Agents. These include a sample RAG agent, a sample text-to-SQL agent, and pharmaceutical research agents that use multi-agent collaboration for cancer biomarker discovery.

SQL 123

SQL AWS Database Machine Learning 123

AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them.

SQL 130

SQL AWS Database Data Scientist 130

AWS Machine Learning Blog

OCTOBER 28, 2024

This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects.

Machine Learning 112

Machine Learning Machine Learning ML ML 112

Flipboard

MAY 28, 2025

This post explores deploying a text-to-SQL pipeline using generative AI models and Amazon Bedrock to ask natural language questions to a genomics database. We demonstrate how to implement an AI assistant web interface with AWS Amplify and explain the prompt engineering strategies adopted to generate the SQL queries.

SQL 97

SQL AWS AI AI 97

AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Basic knowledge of a SQL query editor.

Data Warehouse 117

Data Warehouse Machine Learning Machine Learning Cloud Data 117

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.

SQL 143

SQL AWS Database ML 143

AWS Machine Learning Blog

OCTOBER 24, 2024

Solution overview Typically, a three-tier software application has a UI interface tier, a middle tier (the backend) for business APIs, and a database tier. Generate, run, and validate the SQL from natural language understanding using LLMs, few-shot examples, and a database schema as a knowledge base.

AWS 100

AWS SQL Database AI 100

AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. The following diagram illustrates the solution architecture.

SQL 131

SQL Data Lakes Data Analyst AWS 131

AWS Machine Learning Blog

NOVEMBER 12, 2024

NOTE : Since we used an SQL query engine to query the dataset for this demonstration, the prompts and generated outputs mention SQL below. NOTE : Since we used an SQL query engine to query the dataset for this demonstration, the prompts and generated outputs mention SQL below.

SQL 124

SQL AWS AI AI 124

AWS Machine Learning Blog

AUGUST 1, 2024

By demonstrating the process of deploying fine-tuned models, we aim to empower data scientists, ML engineers, and application developers to harness the full potential of FMs while addressing unique application requirements. We use the sql-create-context dataset available on Hugging Face for fine-tuning.

SQL 130

SQL AWS ML ML 130

AWS Machine Learning Blog

AUGUST 12, 2024

Instead, organizations are increasingly looking to take advantage of transformative technologies like machine learning (ML) and artificial intelligence (AI) to deliver innovative products, improve outcomes, and gain operational efficiencies at scale. Data is presented to the personas that need access using a unified interface.

ML 127

ML ML AWS AI 127

Flipboard

DECEMBER 11, 2024

Second, because data, code, and other development artifacts like machine learning (ML) models are stored within different services, it can be cumbersome for users to understand how they interact with each other and make changes. With the SQL editor, you can query data lakes, databases, data warehouses, and federated data sources.

SQL 160

SQL AWS Data Lakes AI 160

AWS Machine Learning Blog

DECEMBER 6, 2023

With the right underlying embedding model, capable of producing accurate semantic representations of the input document chunks and the input questions, and an efficient semantic search module, this solution is able to answer questions that require retrieving existent information in a database of documents.

SQL 130

SQL AWS Analytics Analytics 130

AWS Machine Learning Blog

JANUARY 15, 2025

By employing a multi-modal approach, the solution connects relevant data elements across various databases. Based on the customer query and context, the system dynamically generates text-to-SQL queries, summarizes knowledge base results using semantic search , and creates personalized vehicle brochures based on the customers preferences.

AWS 107

AWS SQL AI AI 107

AWS Machine Learning Blog

DECEMBER 4, 2024

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. Use Amazon Athena SQL queries to provide insights.

AWS 109

AWS AI AI SQL 109

ODSC - Open Data Science

APRIL 6, 2023

Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL. But why is SQL, or Structured Query Language , so important to learn? Let’s start with the first clause often learned by new SQL users, the WHERE clause.

SQL 98

SQL Data Scientist Database Data Science 98

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. The primary goal is to automatically generate SQL queries from natural language text. What percentage of customers are from each region?”

SQL 141

SQL Database AI AI 141

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.

SQL 94

SQL AWS Database AI 94

Flipboard

AUGUST 17, 2023

Many practitioners are extending these Redshift datasets at scale for machine learning (ML) using Amazon SageMaker , a fully managed ML service, with requirements to develop features offline in a code way or low-code/no-code way, store featured data from Amazon Redshift, and make this happen at scale in a production environment.

ML 123

ML ML AWS Data Warehouse 123

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.

SQL 168

SQL Database AWS Machine Learning 168

Flipboard

JUNE 26, 2023

These techniques utilize various machine learning (ML) based approaches. In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience.

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AWS ML ML ETL 123

AWS Machine Learning Blog

NOVEMBER 26, 2024

Now, consider a different scenario: an AI assistant, designed to assist back desk agents at this travel company, uses an LLM to translate natural language queries into SQL commands. We need every user request mapped accurately to its corresponding SQL command, leaving no room for error. Precision is key here.

AWS 127

AWS Machine Learning Machine Learning AI 127

Flipboard

DECEMBER 18, 2023

Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more. Learn more about the AWS zero-ETL future with newly launched AWS databases integrations with Amazon Redshift.

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AWS Data Warehouse ETL SQL 139

IBM Journey to AI blog

MARCH 14, 2024

Also, traditional database management tasks, including backups, upgrades and routine maintenance drain valuable time and resources, hindering innovation. By using fit-for-purpose databases, customers can efficiently run workloads, using the appropriate engine at the optimal cost to optimize analytics for the best price-performance.

AWS 93

AWS Database ETL AI 93

Flipboard

MAY 8, 2025

For example, with MCP an AI model could fetch information from a database, edit a design in Figma, or control a music app all by sending natural-language instructions through a standardized interface. Developers were dealing with fragmented integrations everywhere. Not only is this labor-intensive; its brittle and doesnt scale.

Database 151

Database AI AI SQL 151

Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning.

Database 158

Database AWS ETL SQL 158

Data Science Dojo

MAY 18, 2023

Additionally, how ML Ops is particularly helpful for large-scale systems like ad auctions, where high data volume and velocity can pose unique challenges. Getting Started with SQL Programming: Are you starting your journey in data science? If you’re new to SQL, this beginner-friendly tutorial is for you!

Data Science 195

Data Science AI AI SQL 195

AWS Machine Learning Blog

NOVEMBER 26, 2024

By harnessing the power of threat intelligence, machine learning (ML), and artificial intelligence (AI), Sophos delivers a comprehensive range of advanced products and services. The Sophos Artificial Intelligence (AI) group (SophosAI) oversees the development and maintenance of Sophos’s major ML security technology.

Machine Learning 102

Machine Learning Machine Learning SQL ML 102

AWS Machine Learning Blog

AUGUST 20, 2024

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. Amazon Q Business analyzes intent, accesses data sources, and generates the SQL query.

SQL 124

SQL AWS Database Natural Language Processing 124

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.

Database 101

Database SQL AWS AI 101

Dataconomy

MAY 2, 2023

What do machine learning engineers do: ML engineers design and develop machine learning models The responsibilities of a machine learning engineer entail developing, training, and maintaining machine learning systems, as well as performing statistical analyses to refine test results. Is ML engineering a stressful job?

ML 110

ML ML Machine Learning Machine Learning 110

AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

ML 98

ML ML Apache Kafka SQL 98

AWS Machine Learning Blog

FEBRUARY 18, 2025

The assistant is connected to internal and external systems, with the capability to query various sources such as SQL databases, Amazon CloudWatch logs, and third-party tools to check the live system health status. This removed a considerable amount of data in the ETL process even before ingesting into the knowledge base.

AWS 75

AWS Database ETL AI 75

phData

NOVEMBER 4, 2024

Dataiku’s join recipe lets you customize how to join tables together From Data to Predictions Using Visual ML Dataiku’s automated feature engineering tools further accelerate the preparation process by automatically generating features based on the dataset’s content. Dataiku and Snowflake: A Good Combo?

Machine Learning 69

Machine Learning Machine Learning Data Science ML 69