AWS Machine Learning Blog

MARCH 1, 2023

Flywheel creates a data lake (in Amazon S3) in your account where all the training and test data for all versions of the model are managed and stored. Periodically, the new labeled data (to retrain the model) can be made available to flywheel by creating datasets. The data can be accessed from AWS Open Data Registry.

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Data Lakes AWS ML ML 97

Data Science Dojo

SEPTEMBER 11, 2024

With this full-fledged solution, you don’t have to spend all your time and effort combining different services or duplicating data. Overview of One Lake Fabric features a lake-centric architecture, with a central repository known as OneLake.

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AWS Machine Learning Blog

MARCH 8, 2023

Data collection and ingestion The data collection and ingestion layer connects to all upstream data sources and loads the data into the data lake. Therefore, the ingestion components need to be able to manage authentication, data sourcing in pull mode, data preprocessing, and data storage.

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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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IBM Journey to AI blog

DECEMBER 7, 2023

Increased operational efficiency benefits Reduced data preparation time : OLAP data preparation capabilities streamline data analysis processes, saving time and resources.

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Data Preparation Database AI Data Analysis 72

Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Data integration.

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Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Data integration.

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Tableau Data Quality Analytics Analytics 98

Pickl AI

FEBRUARY 21, 2023

The data locations may come from the data warehouse or data lake with structured and unstructured data. The Data Scientist’s responsibility is to move the data to a data lake or warehouse for the different data mining processes. are the various data mining tools.

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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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ODSC - Open Data Science

JUNE 12, 2023

No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows. Building business-focussed semantic layers in the cloud (the Power BI Service) with data modeling capabilities, such as managing relationships, creating measures, defining incremental refresh, and creating and managing RLS.

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Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

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Alation

SEPTEMBER 23, 2021

Data Catalogs for Data Science & Engineering – Data catalogs that are primarily used for data science and engineering are typically used by very experienced data practitioners. It also catalogs datasets and operations that includes data preparation features and functions.

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Data Preparation SQL Data Governance Data Analysis 52

phData

SEPTEMBER 28, 2023

Dataflows represent a cloud-based technology designed for data preparation and transformation purposes. Dataflows have different connectors to retrieve data, including databases, Excel files, APIs, and other similar sources, along with data manipulations that are performed using Online Power Query Editor.

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IBM Journey to AI blog

MARCH 14, 2024

Whether it’s for ad hoc analytics, data transformation, data sharing, data lake modernization or ML and gen AI, you have the flexibility to choose. Integrated solutions for zero-ETL data preparation: IBM databases on AWS offer integrated solutions that eliminate the need for ETL processes in data preparation for AI.

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Precisely

JULY 18, 2024

Without access to all critical and relevant data, the data that emerges from a data fabric will have gaps that delay business insights required to innovate, mitigate risk, or improve operational efficiencies. You must be able to continuously catalog, profile, and identify the most frequently used data.

Data Lakes 52

Data Lakes Data Warehouse Data Governance Machine Learning 52

Flipboard

NOVEMBER 24, 2023

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases.

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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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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

Even something like gamification may emerge as a way to fully engage data shoppers as a community. Behind the scenes, ‘backroom services” will power the storefront, performing such tasks as data acquisition, data preparation, data curation and cataloging, and tracking. Building the EDM.

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Data Warehouse Data Lakes Hadoop Data Preparation 52

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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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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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.

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Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

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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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Pickl AI

AUGUST 1, 2023

Key Components of Data Engineering Data Ingestion : Gathering data from various sources, such as databases, APIs, files, and streaming platforms, and bringing it into the data infrastructure. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.

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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.

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phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

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Pickl AI

OCTOBER 10, 2024

Informatica’s AI-powered automation helps streamline data pipelines and improve operational efficiency. Common use cases include integrating data across hybrid cloud environments, managing data lakes, and enabling real-time analytics for Business Intelligence platforms.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

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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Machine Learning Machine Learning Data Preparation AI 52

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.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation. And doing all of that while giving you the best-in-class experience in terms of security and governance.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation. And doing all of that while giving you the best-in-class experience in terms of security and governance.

SQL 52

SQL ML ML Python 52

phData

DECEMBER 4, 2023

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. It might be tempting to have massive files and let the system sort it out.

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The MLOps Blog

OCTOBER 20, 2023

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

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SQL Database Data Scientist Python 52

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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