Pickl AI

NOVEMBER 4, 2024

The global Big Data and Data Engineering Services market, valued at USD 51,761.6 This article explores the key fundamentals of Data Engineering, highlighting its significance and providing a roadmap for professionals seeking to excel in this vital field. What is Data Engineering? million by 2028. from 2025 to 2030.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

DECEMBER 9, 2024

Summary: This article provides a comprehensive guide on Big Data interview questions, covering beginner to advanced topics. Introduction Big Data continues transforming industries, making it a vital asset in 2025. The global Big Data Analytics market, valued at $307.51 What is Apache Kafka, and Why is it Used?

Big Data 52

Big Data Big Data Hadoop Apache Kafka 52

The MLOps Blog

FEBRUARY 5, 2023

There are many platforms and sources that generate this kind of data. In this article, we will go through the basics of streaming data, what it is, and how it differs from traditional data. We will also get familiar with tools that can help record this data and further analyse it.

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

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Big Data Big Data Data Engineering Data Engineer 91

DagsHub

APRIL 7, 2024

Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines. These pipelines cover the entire lifecycle of an ML project, from data ingestion and preprocessing, to model training, evaluation, and deployment. It is lightweight.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. How to properly manage unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

DagsHub

JANUARY 14, 2025

This article is an attempt to delve into how duplicate data can affect machine learning models, and how it impacts their accuracy and other performance metrics. We'll try to uncover practical strategies to identify, analyze, and manage duplicate data effectively. We hope you find this article thought-provoking!

Machine Learning 52

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The MLOps Blog

AUGUST 11, 2023

Today different stages exist within ML pipelines built to meet technical, industrial, and business requirements. This section delves into the common stages in most ML pipelines, regardless of industry or business function. 1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g.,

ML 52

ML ML Machine Learning Machine Learning 52