Experimentation Metadata Testing 
Rocket-Powered Data Science
FEBRUARY 15, 2023
encouraging and rewarding) a culture of experimentation across the organization. Know thy data: understand what it is (formats, types, sampling, who, what, when, where, why), encourage the use of data across the enterprise, and enrich your datasets with searchable (semantic and content-based) metadata (labels, annotations, tags).
AWS Big Data
NOVEMBER 19, 2024
Customers maintain multiple MWAA environments to separate development stages, optimize resources, manage versions, enhance security, ensure redundancy, customize settings, improve scalability, and facilitate experimentation. This approach offers greater flexibility and control over workflow management.
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O'Reilly on Data
MARCH 31, 2020
This has serious implications for software testing, versioning, deployment, and other core development processes. You might have millions of short videos , with user ratings and limited metadata about the creators or content. The ability to make decisions based on data analytics is a prerequisite for an “experimental culture.”
CIO Business Intelligence
NOVEMBER 4, 2022
The company’s multicloud infrastructure has since expanded to include Microsoft Azure for business applications and Google Cloud Platform to provide its scientists with a greater array of options for experimentation. For McCowan, the key is to give scientists any and all tools that allow them to explore their hypotheses and test theories.
O'Reilly on Data
MARCH 23, 2023
But Transformers have some other important advantages: Transformers don’t require training data to be labeled; that is, you don’t need metadata that specifies what each sentence in the training data means. It’s by far the most convincing example of a conversation with a machine; it has certainly passed the Turing test.
Domino Data Lab
MARCH 20, 2019
Models are so different from software — e.g., they require much more data during development, they involve a more experimental research process, and they behave non-deterministically — that organizations need new products and processes to enable data science teams to develop, deploy and manage them at scale.
Occam's Razor
APRIL 8, 2013
Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Testing out a new feature. Identify, hypothesize, test, react. But at the same time, they had to have a real test of an actual feature. You don’t need a beautiful beast to go out and test.
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