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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. A lot to learn, but worthwhile to access the unique and special value AI can create in the product space. Managing Machine Learning Projects” (AWS).

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Allows metadata repositories to share and exchange.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

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].

Metadata 105
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Themes and Conferences per Pacoid, Episode 13

Domino Data Lab

NOAA hosts a unique concentration of the world’s climate science research throughout its labs and other centers, with experts in closely adjacent fields: polar ice, coral reef health, sunny day flooding, ocean acidification, fisheries counts, atmospheric C02, sea-level rise, ocean currents, and so on. Metadata Challenges.

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Natural Language in Python using spaCy: An Introduction

Domino Data Lab

We can compare open source licenses hosted on the Open Source Initiative site: In [11]: lic = {} ?lic["mit"] metadata=convention_df["speaker"]? ). Another big change occurred during 2017-2018 when, following the many successes of deep learning, those approaches began to out-perform previous machine learning models.