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How DoorDash Migrated from StatsD to Prometheus

DoorDash Engineering

Challenges Faced With StatsD StatsD was a great asset for our early observability needs, but we began encountering constraints such as losing metrics during surge events, difficulties with naming/standardized tags, and a lack of reporting tools. These common tags are useful to create common dashboards and alerts to monitor service health.

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Web Scraping Using R.!

Data Science Blog: Data Engineering

In this blog, I’ll show you, How to Web Scrape using R.? R is a programming language and its environment built for statistical analysis, graphical representation & reporting. R programming is mostly preferred by statisticians, data miners, and software programmers who want to develop statistical software. What is R.?

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From Big Data to Better Data: Ensuring Data Quality with Verity

Lyft Engineering

Finally, as the subject of this blog post, we can assess data quality via batch compute analytics on our data warehouse, providing a comprehensive albeit slower evaluation compared to the previously mentioned methods. It can be a fixed threshold or a statistical one. This has useful aggregations like owning team, table name, and tags.

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Natural Language Processing: A Guide to NLP Use Cases, Approaches, and Tools

AltexSoft

Using linguistics, statistics, and machine learning, computers can not only derive meaning from what’s said or written, but also catch contextual nuances and a person’s intent and sentiment in the same way humans do. Information from an invoice is extracted, tagged, and structured. Statistical NLP vs deep learning.

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Machine Learning (ML) vs NLP - What's the Difference?

ProjectPro

Machine learning or ML is a sub-field of artificial intelligence that uses statistical techniques to solve large amounts of data without any human intervention. POS-Tagging : Also called Parts of Speech Tagging, is an ML technique that tags parts of speech like nouns, verbs, etc., This is called supervised learning.

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Fraud Detection with Cloudera Stream Processing Part 1

Cloudera

In a previous blog of this series, Turning Streams Into Data Products , we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. This blog will be published in two parts. This is what we call the first-mile problem.

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Why teach MLOps to your Data Science Teams?

DareData

Of course, this blog only exposes techniques regarding the construction and maintenance of Machine Learning models, one should never forget the fundamental statistical pillars guiding ML usage –for example, see /dsbuildingblocks-correlationcausality/. It can call tasks and other flows –named subflows.