Remove 2014 Remove Clustering Remove Data Engineering
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Top 5 Use Cases of phData’s Advisor Tool

phData

Founded in 2014 by three leading cloud engineers, phData focuses on solving real-world data engineering, operations, and advanced analytics problems with the best cloud platforms and products. Over the years, one of our primary focuses became Snowflake and migrating customers to this leading cloud data platform.

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

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Must-Have Prompt Engineering Skills for 2024

ODSC - Open Data Science

These outputs, stored in vector databases like Weaviate, allow Prompt Enginers to directly access these embeddings for tasks like semantic search, similarity analysis, or clustering. GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN.

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Understanding and predicting urban heat islands at Gramener using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

Among these models, the spatial fixed effect model yielded the highest mean R-squared value, particularly for the timeframe spanning 2014 to 2020. SageMaker Processing enables the flexible scaling of compute clusters to accommodate tasks of varying sizes, from processing a single city block to managing planetary-scale workloads.

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10 takeaways from 10 years of data science for social good

DrivenData Labs

Looking back ¶ When we started DrivenData in 2014, the application of data science for social good was in its infancy. There was rapidly growing demand for data science skills at companies like Netflix and Amazon. Deep learning - It is hard to overstate how deep learning has transformed data science.

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What is the Snowflake Data Cloud and How Much Does it Cost?

phData

Effectively this is a way to store the source of truth and build (or rebuild) your downstream data products (including data warehouses) from it. What is the Difference Between a Data Lake and a Data Warehouse? Historically, there were big differences.

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Analyzing the history of Tableau innovation

Tableau

Clustered under visual encoding , we have topics of self-service analysis , authoring , and computer assistance. Connecting to data is fundamental to all data work, which is why “get data'' is at the start of the Cycle of Visual Analysis. The Data Tab was added in v8.2 Let’s take a look at each. . Connectivity.

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