Data Science Blog

MAY 20, 2024

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. They transform data into a consistent format for users to consume.

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Data Pipeline Data Warehouse Azure Data Lakes 262

IBM Data Science in Practice

MARCH 8, 2023

Hidden Technical Debt in Machine Learning Systems More money, more problems — Rise of too many ML tools 2012 vs 2023 — Source: Matt Turck People often believe that money is the solution to a problem. A feature platform should automatically process the data pipelines to calculate that feature. Spark, Flink, etc.)

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Machine Learning Machine Learning ML ML 264

Smart Data Collective

OCTOBER 17, 2022

In fact, you may have even heard about IDC’s new Global DataSphere Forecast, 2021-2025 , which projects that global data production and replication will expand at a compound annual growth rate of 23% during the projection period, reaching 181 zettabytes in 2025. zettabytes of data in 2020, a tenfold increase from 6.5

Big Data 119

Big Data Big Data Data Engineering Data Engineering 119

Alation

JULY 7, 2022

Enterprises were collecting vast ecosystems of data, and began regarding them, for the first time, as worlds worthy of exploration. The data scientist. In 2012 Davenport and Patil declared the data scientist was “ The Sexiest Job of the 21st Century.” programs in Information Science and Data Analytics.

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Data Scientist Data Analyst Analytics Analytics 52

AWS Machine Learning Blog

OCTOBER 24, 2024

RAG introduces additional data engineering requirements: Scalable retrieval indexes must ingest massive text corpora covering requisite knowledge domains. Data must be preprocessed to enable semantic search during inference. Data pipelines must seamlessly integrate new data at scale.

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AWS Data Pipeline Database Big Data 109