AWS Machine Learning Blog

AUGUST 1, 2024

In this post, we demonstrate the process of fine-tuning Meta Llama 3 8B on SageMaker to specialize it in the generation of SQL queries (text-to-SQL). Solution overview We walk through the steps of fine-tuning an FM with using SageMaker, and importing and evaluating the fine-tuned FM for SQL query generation using Amazon Bedrock.

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Data Science Dojo

MARCH 7, 2023

This includes sourcing, gathering, arranging, processing, and modeling data, as well as being able to analyze large volumes of structured or unstructured data. The goal of data preparation is to present data in the best forms for decision-making and problem-solving.

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Data Science Dojo

FEBRUARY 9, 2023

They use various tools and techniques to extract insights from data, such as statistical analysis, and data visualization. They may also work with databases and programming languages such as SQL and Python to manipulate and extract data. Check out this course and learn Power BI today!

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Data Science Dojo

JUNE 7, 2023

The primary aim is to make sense of the vast amounts of data generated daily by combining statistical analysis, programming, and data visualization. It is divided into three primary areas: data preparation, data modeling, and data visualization.

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Dataconomy

APRIL 25, 2025

Dplyr is an essential package in R programming, particularly beneficial for data manipulation tasks. It streamlines data preparation and analysis, making it easier for data scientists and analysts to extract insights from their datasets. dbplyr : Allows dplyr functions to interface with SQL databases.

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Flipboard

DECEMBER 11, 2024

Data processing and SQL analytics Analyze, prepare, and integrate data for analytics and AI using Amazon Athena, Amazon EMR, AWS Glue, and Amazon Redshift. Data and AI governance Publish your data products to the catalog with glossaries and metadata forms. The SQL ran on AWS Glue for Spark.

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Dataconomy

MARCH 5, 2025

Qualifications and required skills A robust educational foundation and skill set are essential for data scientists: Educational background: Most data scientists have a bachelor’s degree in a related field, with a substantial portion holding masters degrees.

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phData

NOVEMBER 4, 2024

With data software pushing the boundaries of what’s possible in order to answer business questions and alleviate operational bottlenecks, data-driven companies are curious how they can go “beyond the dashboard” to find the answers they are looking for. One of the standout features of Dataiku is its focus on collaboration.

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Data Science Dojo

SEPTEMBER 11, 2024

In the sales context, this ensures that sales data remains consistent, accurate, and easily accessible for analysis and reporting. Synapse Data Science: Synapse Data Science empowers data scientists to work directly with secured and governed sales data prepared by engineering teams, allowing for the efficient development of predictive models.

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Pickl AI

APRIL 10, 2023

Data Analysis is one of the most crucial tasks for business organisations today. SQL or Structured Query Language has a significant role to play in conducting practical Data Analysis. That’s where SQL comes in, enabling data analysts to extract, manipulate and analyse data from multiple sources.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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Dataconomy

MARCH 19, 2025

Key disciplines involved in data science Understanding the core disciplines within data science provides a comprehensive perspective on the field’s multifaceted nature. Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics.

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Tableau

JULY 28, 2020

Ryan Cairnes Senior Manager, Product Management, Tableau Hannah Kuffner July 28, 2020 - 10:43pm March 20, 2023 Tableau Prep is a citizen data preparation tool that brings analytics to anyone, anywhere. With Prep, users can easily and quickly combine, shape, and clean data for analysis with just a few clicks.

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Tableau

JULY 28, 2020

Ryan Cairnes Senior Manager, Product Management, Tableau Hannah Kuffner July 28, 2020 - 10:43pm March 20, 2023 Tableau Prep is a citizen data preparation tool that brings analytics to anyone, anywhere. With Prep, users can easily and quickly combine, shape, and clean data for analysis with just a few clicks.

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Data Preparation Tableau Database Clean Data 52

AWS Machine Learning Blog

DECEMBER 1, 2023

Additionally, these tools provide a comprehensive solution for faster workflows, enabling the following: Faster data preparation – SageMaker Canvas has over 300 built-in transformations and the ability to use natural language that can accelerate data preparation and making data ready for model building.

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Data Science Dojo

AUGUST 16, 2024

Natural Language Processing (NLP) for Data Interaction Generative AI models like GPT-4 utilize transformer architectures to understand and generate human-like text based on a given context. Personalized Reporting : Perfect for managers and executives who need quick, relevant updates on key metrics without delving into complex data sets.

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IBM Journey to AI blog

OCTOBER 30, 2024

Integrated vectorized embedding capabilities streamline data preparation for various applications such as retrieval augmented generation (RAG) and other machine learning and generative AI use cases.

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How to Learn Machine Learning

APRIL 26, 2025

Data can be generated from databases, sensors, social media platforms, APIs, logs, and web scraping. Data can be in structured (like tables in databases), semi-structured (like XML or JSON), or unstructured (like text, audio, and images) form. How to Choose the Right Data Science Career Path?

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ODSC - Open Data Science

SEPTEMBER 25, 2023

Tapping into these schemas and pulling out machine learning-ready features can be nontrivial as one needs to know where the data entity of interest lives (e.g., customers), what its relations are, and how they’re connected, and then write SQL, python, or other to join and aggregate to a granularity of interest.

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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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AWS Machine Learning Blog

NOVEMBER 1, 2024

We discuss the important components of fine-tuning, including use case definition, data preparation, model customization, and performance evaluation. This post dives deep into key aspects such as hyperparameter optimization, data cleaning techniques, and the effectiveness of fine-tuning compared to base models.

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AWS Machine Learning Blog

APRIL 17, 2023

In other words, companies need to move from a model-centric approach to a data-centric approach.” – Andrew Ng A data-centric AI approach involves building AI systems with quality data involving data preparation and feature engineering. Custom transforms can be written as separate steps within Data Wrangler.

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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. Creating ETL pipelines to transform log data Preparing your data to provide quality results is the first step in an AI project.

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AWS Machine Learning Blog

MARCH 8, 2023

We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket. All code for this post is available in the GitHub repo. execute(create_table_sql) print(f"snowflake_table={snowflake_table}") conn.cursor().execute('TRUNCATE

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Data Science Dojo

JANUARY 12, 2023

Insights of data warehouse A data warehouse is a database designed for the analysis of relational data from corporate applications and transactional systems. The results of rapid SQL queries are often utilized for operational reporting and analysis; thus, the data structure and schema are set in advance to optimize for this.

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Data preparation SageMaker Ground Truth employs a human workforce made up of Northpower volunteers to annotate a set of 10,000 images. The model was then fine-tuned with training data from the data preparation stage.

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AWS Machine Learning Blog

MARCH 8, 2023

Therefore, the ingestion components need to be able to manage authentication, data sourcing in pull mode, data preprocessing, and data storage. Because the data is being fetched hourly, a mechanism is also required to orchestrate and schedule ingestion jobs. Data comes from disparate sources in a number of formats.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

Data preparation is important at multiple stages in Retrieval Augmented Generation ( RAG ) models. Create a dataflow Complete the following steps to create a data flow in SageMaker Canvas: On the SageMaker Canvas home page, choose Data preparation. This will land on a data flow page. Choose your domain.

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ODSC - Open Data Science

MARCH 13, 2023

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

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ODSC - Open Data Science

FEBRUARY 5, 2025

Common Pitfalls in LLM Development Neglecting Data Preparation: Poorly prepared data leads to subpar evaluation and iterations, reducing generalizability and stakeholder confidence. Real-world applications often expose gaps that proper data preparation could have preempted. Evaluation: Tools likeNotion.

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Pickl AI

FEBRUARY 4, 2024

The platform employs an intuitive visual language, Alteryx Designer, streamlining data preparation and analysis. With Alteryx Designer, users can effortlessly input, manipulate, and output data without delving into intricate coding, or with minimal code at most. Alteryx’s core features 1.

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AWS Machine Learning Blog

MARCH 10, 2023

Finally, they can also train and deploy models with SageMaker Autopilot , schedule jobs, or operationalize data preparation in a SageMaker Pipeline from Data Wrangler’s visual interface. Solution overview With SageMaker Studio setups, data professionals can quickly identify and connect to existing EMR clusters.

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Dataconomy

JULY 28, 2023

These tools offer a wide range of functionalities to handle complex data preparation tasks efficiently. The tool also employs AI capabilities for automatically providing attribute names and short descriptions for reports, making it easy to use and efficient for data preparation.

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AWS Machine Learning Blog

JUNE 17, 2024

With SageMaker Processing jobs, you can use a simplified, managed experience to run data preprocessing or postprocessing and model evaluation workloads on the SageMaker platform. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB. For more information on processing jobs, see Process data.

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AWS Machine Learning Blog

OCTOBER 19, 2023

Using Amazon Comprehend to redact PII as part of a SageMaker Data Wrangler data preparation workflow keeps all downstream uses of the data, such as model training or inference, in alignment with your organization’s PII requirements. For more details, refer to Integrating SageMaker Data Wrangler with SageMaker Pipelines.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. Data professionals such as data scientists want to use the power of Apache Spark , Hive , and Presto running on Amazon EMR for fast data preparation; however, the learning curve is steep.

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