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Bringing an AI Product to Market

O'Reilly on Data

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 363
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Data Governance and Strategy for the Global Enterprise

Cloudera

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. . Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use.

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Of Muffins and Machine Learning Models

Cloudera

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. So, we have workspaces, projects and sessions in that order.

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AI adoption in the enterprise 2020

O'Reilly on Data

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. Respondent demographics. AI tools organizations are using.

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Data Science, Past & Future

Domino Data Lab

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.