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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Generative AI Prompt Engineer Prompt Engineering ML 120

Applied Data Science

MAY 6, 2021

To keep myself sane, I use Airflow to automate tasks with simple, reusable pieces of code for frequently repeated elements of projects, for example: Web scraping ETL Database management Feature building and data validation And much more! link] We finally have the definition of the DAG. It’s a lot of stuff to stay on top of, right?

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Pickl AI

JANUARY 22, 2025

DDL Interpreter: It processes Data Definition Language (DDL) statements, which define database system structure. Data Dictionary: This repository contains metadata about database objects, such as tables and columns. Their expertise is crucial in projects involving data extraction, transformation, and loading (ETL) processes.

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Data Integration ETL Metadata Data Extraction 52

Pickl AI

JANUARY 27, 2025

Definition of HDFS HDFS is an open-source file system that manages files across a cluster of commodity servers. NameNode The NameNode is your HDFS cluster’s central authority, maintaining the file systems directory tree and metadata. Below are three fundamental components defining the overall core architecture of HDFS.

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The MLOps Blog

AUGUST 3, 2023

At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. I term it as a feature definition store. How is DAGWorks different from other popular solutions? Stefan: You’re exactly right.

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ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

MAY 31, 2023

You may also like Building a Machine Learning Platform [Definitive Guide] Consideration for data platform Setting up the Data Platform in the right way is key to the success of an ML Platform. 2 It also helps to standardize feature definitions across teams. It stores the model weights and maintains a history of model versions.

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ML Algorithm Data Drift Machine Learning 59

The MLOps Blog

MARCH 28, 2023

quality attributes) and metadata enrichment (e.g., Each time they modify the code, the definition of the pipeline changes. Machine learning use cases at Brainly The AI department at Brainly aims to build a predictive intervention system for its users. They have production and training code bases in GitHub repositories.

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Machine Learning Data Scientist Automation ML 59

Pickl AI

JUNE 11, 2024

Data Extraction, Transformation, and Loading (ETL) This is the workhorse of architecture. ETL tools act like skilled miners , extracting data from various source systems. Metadata This acts like the data dictionary, providing crucial information about the data itself. This ensures data accuracy and consistency across the board.

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Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. The table metadata is managed by Data Catalog. This is a SageMaker Lakehouse managed catalog backed by RMS storage.

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