AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. The data lake environment is required to configure an AWS Glue database table, which is used to publish an asset in the Amazon DataZone catalog.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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IBM Journey to AI blog

DECEMBER 7, 2023

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. Early OLAP systems were separate, specialized databases with unique data storage structures and query languages.

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Tableau

APRIL 18, 2022

Instead of centralizing data stores, data fabrics establish a federated environment and use artificial intelligence and metadata automation to intelligently secure data management. . At Tableau, we believe that the best decisions are made when everyone is empowered to put data at the center of every conversation.

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Tableau

APRIL 18, 2022

Instead of centralizing data stores, data fabrics establish a federated environment and use artificial intelligence and metadata automation to intelligently secure data management. . At Tableau, we believe that the best decisions are made when everyone is empowered to put data at the center of every conversation.

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phData

FEBRUARY 24, 2023

Alteryx and the Snowflake Data Cloud offer a potential solution to this issue and can speed up your path to Analytics. In this blog post, we will explore how Alteryx and Snowflake can accelerate your journey to Analytics by sharing use cases and best practices. What is Alteryx? What is Snowflake?

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IBM Journey to AI blog

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. It enables secure data sharing for analytics and AI across your ecosystem.

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IBM Data Science in Practice

JANUARY 9, 2024

In our scenario, the data is stored in the Cloud Object Storage in Watson Studio. However, in a real use case you could receive this data from third party DBs which could be connected directly to IoT Platform. Step 2: MAS Asset/Device Registration Step 2 is crucial to store information on failure history and installation dates etc.

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Flipboard

NOVEMBER 24, 2023

This offering enables BMW ML engineers to perform code-centric data analytics and ML, increases developer productivity by providing self-service capability and infrastructure automation, and tightly integrates with BMW’s centralized IT tooling landscape.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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Pickl AI

FEBRUARY 21, 2023

It involves using statistical and computational techniques to identify patterns and trends in the data that are not readily apparent. Data mining is often used in conjunction with other data analytics techniques, such as machine learning and predictive analytics, to build models that can be used to make predictions and inform decision-making.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. If you want to do the process in a low-code/no-code way, you can follow option C.

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Alation

FEBRUARY 13, 2020

This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata. Data catalogs have become the standard for metadata management in the age of big data and self-service analytics.

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AWS Machine Learning Blog

JUNE 18, 2024

The output data is transformed to a standardized format and stored in a single location in Amazon S3 in Parquet format, a columnar and efficient storage format. With AWS Glue custom connectors, it’s effortless to transfer data between Amazon S3 and other applications.

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Alation

SEPTEMBER 23, 2021

The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset. It offers a broad range of data intelligence solutions, including analytics, data governance, privacy, and cloud transformation. Analytical environments are increasingly complex.

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IBM Journey to AI blog

JULY 17, 2023

Visual modeling: Delivers easy-to-use workflows for data scientists to build data preparation and predictive machine learning pipelines that include text analytics, visualizations and a variety of modeling methods. ” Vitaly Tsivin, EVP Business Intelligence at AMC Networks.

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Precisely

JULY 18, 2024

While data fabric is not a standalone solution, critical capabilities that you can address today to prepare for a data fabric include automated data integration, metadata management, centralized data governance, and self-service access by consumers. Increase metadata maturity.

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ODSC - Open Data Science

JUNE 12, 2023

The Datamarts capability opens endless possibilities for organizations to achieve their data analytics goals on the Power BI platform. No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows. Therefore, Datamarts are not a replacement for Dataflows. A replacement for datasets.

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IBM Journey to AI blog

MAY 9, 2023

It offers its users advanced machine learning, data management , and generative AI capabilities to train, validate, tune and deploy AI systems across the business with speed, trusted data, and governance. It helps facilitate the entire data and AI lifecycle, from data preparation to model development, deployment and monitoring.

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Alation

FEBRUARY 20, 2020

Dave Wells, analyst with the Eckerson Group suggests that realizing the promise of the data warehouse requires a paradigm shift in the way we think about data along with a change in how we access and use it. Self-service analytics environments are giving rise to the data marketplace. Building the EDM.

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ODSC - Open Data Science

NOVEMBER 19, 2024

Despite the rise of big data technologies and cloud computing, the principles of dimensional modeling remain relevant. This session delved into how these traditional techniques have adapted to data lakes and real-time analytics, emphasizing their enduring importance for building scalable, efficient data systems.

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AWS Machine Learning Blog

JUNE 22, 2023

In LnW Connect, an encryption process was designed to provide a secure and reliable mechanism for the data to be brought into an AWS data lake for predictive modeling. Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering.

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Pickl AI

NOVEMBER 4, 2024

Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. It involves developing data pipelines that efficiently transport data from various sources to storage solutions and analytical tools. ETL is vital for ensuring data quality and integrity.

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Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. BI tools rely on high-quality, consistent data to generate accurate insights.

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AWS Machine Learning Blog

AUGUST 4, 2023

Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler. The real-time inference call data is first passed to the SageMaker Data Wrangler container in the inference pipeline, where it is preprocessed and passed to the trained model for product recommendation.

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Pickl AI

SEPTEMBER 5, 2024

Introduction Data Science is revolutionising industries by extracting valuable insights from complex data sets, driving innovation, and enhancing decision-making. This roadmap aims to guide aspiring Azure Data Scientists through the essential steps to build a successful career.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Mai-Lan Tomsen Bukovec, Vice President, Technology | AIM250-INT | Putting your data to work with generative AI Thursday November 30 | 12:30 PM – 1:30 PM (PST) | Venetian | Level 5 | Palazzo Ballroom B How can you turn your data lake into a business advantage with generative AI? You must bring your laptop to participate.

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Alation

FEBRUARY 13, 2020

Data Literacy—Many line-of-business people have responsibilities that depend on data analysis but have not been trained to work with data. Their tendency is to do just enough data work to get by, and to do that work primarily in Excel spreadsheets. Who needs data literacy training? Who can provide the training?

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The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. A self-service infrastructure portal for infrastructure and governance.

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Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. Because that’s the data that’s going to be training the model. And if the data has those biases in them, the trained model will also have those biases embedded in it. It can cover the gamut.

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Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. Because that’s the data that’s going to be training the model. And if the data has those biases in them, the trained model will also have those biases embedded in it. It can cover the gamut.

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phData

DECEMBER 4, 2023

However, for analytics warehouses, you may need to scale for usage. If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Knowing this, you want to have data prepared in a way to optimize your load.

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AWS Machine Learning Blog

JANUARY 26, 2024

Also consider using Amazon Security Lake to automatically centralize security data from AWS environments, SaaS providers, on premises, and cloud sources into a purpose-built data lake stored in your account.

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AWS Machine Learning Blog

AUGUST 15, 2024

Importing data from the SageMaker Data Wrangler flow allows you to interact with a sample of the data before scaling the data preparation flow to the full dataset. This improves time and performance because you don’t need to work with the entirety of the data during preparation.

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Alation

FEBRUARY 20, 2020

Data lakes, while useful in helping you to capture all of your data, are only the first step in extracting the value of that data. We recently announced an integration with Trifacta to seamlessly integrate the Alation Data Catalog with self-service data prep applications to help you solve this issue.

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DataRobot Blog

OCTOBER 3, 2017

This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance. 2) When data becomes information, many (incremental) use cases surface. He is designing data architectures and is looking to prep and clean the data as part of the migration.

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Dataconomy

FEBRUARY 26, 2025

Through various techniques, it allows companies to extract meaningful insights from data, leading to improved strategies and outcomes across different sectors. The importance of data mining Data mining plays a critical role in organizations by enhancing analytics initiatives and supporting various business functions across different sectors.

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