O'Reilly on Data
JULY 28, 2020
Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.
O'Reilly on Data
MARCH 31, 2020
You might have millions of short videos , with user ratings and limited metadata about the creators or content. Job postings have a much shorter relevant lifetime than movies, so content-based features and metadata about the company, skills, and education requirements will be more important in this case.
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Cloudera
FEBRUARY 16, 2022
SDX provides open metadata management and governance across each deployed environment by allowing organisations to catalogue, classify as well as control access to and manage all data assets. Further auditing can be enabled at a session level so administrators can request key metadata about each CML process. Figure 03: lineage.yaml.
O'Reilly on Data
MARCH 18, 2020
Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. It seems as if the experimental AI projects of 2019 have borne fruit. Supervised learning is dominant, deep learning continues to rise.
Domino Data Lab
JULY 2, 2019
In other words, using metadata about data science work to generate code. One of the longer-term trends that we’re seeing with Airflow , and so on, is to externalize graph-based metadata and leverage it beyond the lifecycle of a single SQL query, making our workflows smarter and more robust. BTW, videos for Rev2 are up: [link].
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