The MLOps Blog

JANUARY 23, 2023

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. First of all, machine learning engineers and data scientists often use data from different data vendors.

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phData

SEPTEMBER 26, 2023

Sources The sources involved could influence or determine the options available for the data ingestion tool(s). These could include other databases, data lakes, SaaS applications (e.g. Salesforce), Access databases, SharePoint, or Excel spreadsheets. Data flows from the current data platform to the destination.

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ODSC - Open Data Science

JULY 11, 2023

What are the similarities and differences between data centers, data lake houses, and data lakes? Data centers, data lake houses, and data lakes are all related to data storage and management, but they have some key differences. Not a cloud computer?

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IBM Journey to AI blog

JULY 17, 2023

Through workload optimization across multiple query engines and storage tiers, organizations can reduce data warehouse costs by up to 50 percent. 1 Watsonx.data offers built-in governance and automation to get to trusted insights within minutes, and integrations with existing databases and tools to simplify setup and user experience.

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The MLOps Blog

OCTOBER 20, 2023

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

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SQL Database Data Scientist Python 52

phData

SEPTEMBER 20, 2023

A data mesh is a conceptual architectural approach for managing data in large organizations. Traditional data management approaches often involve centralizing data in a data warehouse or data lake, leading to challenges like data silos, data ownership issues, and data access and processing bottlenecks.

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Data Silos Database Data Quality Data Engineering 52

Snorkel AI

MAY 26, 2023

And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation. And then the production teams might be leveraging a totally different single source of truth or data warehouse or data lake and totally different compute infrastructure for deploying models into production.

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Snorkel AI

MAY 26, 2023

And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation. And then the production teams might be leveraging a totally different single source of truth or data warehouse or data lake and totally different compute infrastructure for deploying models into production.

SQL 52

SQL ML ML Python 52