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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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

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Big Data Big Data Data Engineering Data Engineer 119

Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Above all, this solution offers you a native Spark way to implement an end-to-end data pipeline from Amazon Redshift to SageMaker.

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ML ML AWS Data Warehouse 123

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.” Who would uncover secrets from these unknown landscapes?

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Data Scientist Data Analyst Analytics Analytics 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Around this time, industry observers reported NVIDIA’s strategy pivoting from its traditional gaming and graphics focus to moving into scientific computing and data analytics. in 2012 is now widely referred to as ML’s “Cambrian Explosion.” An important part of the data pipeline is the production of features, both online and offline.

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DataRobot

SEPTEMBER 14, 2021

Since DataRobot was founded in 2012, we’ve been committed to democratizing access to the power of AI. We’re building a platform for all users: data scientists, analytics experts, business users, and IT. Let’s dive into each of these areas and talk about how we’re delivering the DataRobot AI Cloud Platform with our 7.2

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AI AI Data Pipeline Data Preparation 52