DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. A primer on ML workflows and pipelines Before exploring the tools, we first need to explain the difference between ML workflows and pipelines.

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DagsHub

OCTOBER 23, 2024

Unstructured data makes up 80% of the world's data and is growing. 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.

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DagsHub

JANUARY 14, 2025

In today's data-driven world, machine learning practitioners often face a critical yet underappreciated challenge: duplicate data management. A massive amount of diverse data powers today's ML models. You will find sections on managing duplicate data, best practices, current trends and so on.

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ODSC - Open Data Science

FEBRUARY 6, 2024

Spark offers a versatile range of functionalities, from batch processing to stream processing, making it a comprehensive solution for complex data challenges. Apache Kafka For data engineers dealing with real-time data, Apache Kafka is a game-changer.

Data Engineering 52

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The MLOps Blog

FEBRUARY 5, 2023

We will also get familiar with tools that can help record this data and further analyse it. In the later part of this article, we will discuss its importance and how we can use machine learning for streaming data analysis with the help of a hands-on example. What is streaming data?

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Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

Data Engineering 52

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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.

Big Data 91

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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Pickl AI

JULY 25, 2024

Improved Decision-making By providing a consolidated and accessible view of data, organisations can identify trends, patterns, and anomalies more quickly, leading to better-informed and timely decisions. Data Ingestion Tools To facilitate the process, various tools and technologies are available.

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The MLOps Blog

AUGUST 11, 2023

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Many questions regarding building machine learning pipelines and systems have already been answered and come from industry best practices and patterns. How should the machine learning pipeline operate?

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phData

SEPTEMBER 27, 2024

Looking to build a machine-learning model for churn prediction? The atomic data provides a perfect input, capturing the full richness of customer behavior over time. Technologies like Apache Kafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time.

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