Data Engineering Metrics Off-The-Shelf 
CIO
NOVEMBER 27, 2024
Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.
O'Reilly Media - Data
MARCH 22, 2018
Data Scientist Cathy O’Neil has recently written an entire book filled with examples of poor interpretability as a dire warning of the potential social carnage from misunderstood models—e.g., There is also a trade off in balancing a model’s interpretability and its performance.
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Altexsoft
OCTOBER 3, 2019
Mark Huselid and Dana Minbaeva in Big Data and HRM call these measures the understanding of the workforce quality. The day may come when a seasoned professional tells you or your colleague about their plan to leave the company in a month. This situation isn’t extraordinary: managers and HR specialists of any organization have been there.
AWS Machine Learning - AI
MARCH 18, 2025
However, off-the-shelf LLMs cant be used without some modification. Additionally, the complexity increases due to the presence of synonyms for columns and internal metrics available. RAG works by using a retriever module to find relevant information from an external data store in response to a users prompt.
O'Reilly Media - Ideas
MARCH 31, 2020
We won’t go into the mathematics or engineering of modern machine learning here. All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data.
The Accidental Successful CIO
MAY 12, 2021
These BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps designed to provide users with detailed intelligence about the state of the business. The challenge that CIOs are facing is how best to make use of these new tools?
Cloudera
JULY 24, 2023
In their effort to reduce their technology spend, some organizations that leverage open source projects for advanced analytics often consider either building and maintaining their own runtime with the required data processing engines or retaining older, now obsolete, versions of legacy Cloudera runtimes (CDH or HDP).
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