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.

Marketing 362

Marketing Experimentation Metrics Testing 362

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.

Management 332

Management Machine Learning Experimentation Metrics 332

Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. Machine Learning Model Visibility . Figure 03: lineage.yaml.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

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.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311