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.

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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.

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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

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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

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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.

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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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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 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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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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Towards AI

AUGUST 29, 2024

Querying SQL Database Using LLM Agents — Is It a Good Idea? by Sachin Khandewal This blog explains different ways to query SQL Databases using Groq to access the LLMs. It also explains how to leverage LLM Agents to build an SQL Agent using an advanced DSPy framework and highlights its limitations.

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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.

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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.

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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!

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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?

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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?

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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.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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IBM Journey to AI blog

APRIL 13, 2023

Building ML infrastructure and integrating ML models with the larger business are major bottlenecks to AI adoption [1,2,3]. IBM Db2 can help solve these problems with its built-in ML infrastructure. In this post, I will show how to develop, deploy, and use a decision tree model in a Db2 database.

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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.

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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

MAY 1, 2024

This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. To retrieve data from database, you can use foundation models (FMs) offered by Amazon Bedrock, converting text into SQL queries with specified constraints.

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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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Mlearning.ai

JULY 20, 2023

Source —  [link] SQL is one of the hardcore requirements for any software or analytical system — without which today’s systems will cease to exist. Writing an efficient SQL query is as important as having it. Having an inefficient SQL query will block up the operation of any software system thus rendering it in a broken state.

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Tableau

DECEMBER 1, 2021

Chris had earned an undergraduate computer science degree from Simon Fraser University and had worked as a database-oriented software engineer. In 2004, Tableau got both an initial series A of venture funding and Tableau’s first EOM contract with the database company Hyperion—that’s when I was hired. Release v1.0

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AWS Machine Learning Blog

MAY 30, 2024

In this post, we describe how CBRE partnered with AWS Prototyping to develop a custom query environment allowing natural language query (NLQ) prompts by using Amazon Bedrock, AWS Lambda , Amazon Relational Database Service (Amazon RDS), and Amazon OpenSearch Service. The wrapper function runs the SQL query using psycopg2.

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Data Science Dojo

JANUARY 25, 2023

Building a team with skillsets Your team mix should include the following: Data scientists who can apply ML techniques. Remove any empty rows, cells, or other data that isn’t relevant or useful for your database. Additionally, it protects against common web-based attacks, such as SQL injection and cross-site scripting.

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Mlearning.ai

JUNE 17, 2023

At present, there’s a growing buzz around Vector Databases. Vector databases are a vast and complex topic, and discussing them in detail is beyond the scope of this article. In this case, we’ll demonstrate its use for understanding and querying databases. We will download it, and stored on a database for our use.

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The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. Reusability & reproducibility: Building ML models is time-consuming by nature. Save vs package vs store ML models Although all these terms look similar, they are not the same.

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