O'Reilly on Data

JULY 28, 2020

This tradeoff between impact and development difficulty is particularly relevant for products based on deep learning: breakthroughs often lead to unique, defensible, and highly lucrative products, but investing in products with a high chance of failure is an obvious risk. arbitrary stemming, stop word removal.). Conclusion.

Marketing 362

Marketing Experimentation Metrics Testing 362

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 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

IBM Big Data Hub

FEBRUARY 14, 2024

The IBM team is even using generative AI to create synthetic data to build more robust and trustworthy AI models and to stand in for real-world data protected by privacy and copyright laws. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions.

Cost-Benefit 91

Cost-Benefit Insurance Unstructured Data Machine Learning 91

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. Supervised learning is dominant, deep learning continues to rise. AI tools organizations are using.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Domino Data Lab

OCTOBER 9, 2019

We’ll examine National Oceanic and Atmospheric Administration (NOAA) data management practices which I learned about at their workshop, as a case study in how to handle data collection, dataset stewardship, quality control, analytics, and accountability when the stakes are especially high. Metadata Challenges.

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

Domino Data Lab

JULY 22, 2019

We’ve got this complex landscape, tons of data sharing, an economy of data, external data, tons of mobile devices. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. You can take TensorFlow.js

Data Science 109

Data Science Machine Learning Data Governance Modeling 109