Remove Machine Learning Remove Metadata Remove Publishing
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Neptune.ai?—?A Metadata Store for MLOps

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. A centralized location for research and production teams to govern models and experiments by storing metadata throughout the ML model lifecycle. A Metadata Store for MLOps appeared first on Analytics Vidhya. Keeping track of […]. The post Neptune.ai?—?A

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Underlying Engineering Behind Alexa’s Contextual ASR

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Any type of contextual information, like device context, conversational context, and metadata, […]. Any type of contextual information, like device context, conversational context, and metadata, […].

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The state of data quality in 2020

O'Reilly on Data

Just 20% of organizations publish data provenance and data lineage. Almost half (48%) of respondents say they use data analysis, machine learning, or AI tools to address data quality issues. These include the basics, such as metadata creation and management, data provenance, data lineage, and other essentials.

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Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality

AWS Big Data

The importance of publishing only high-quality data cant be overstatedits the foundation for accurate analytics, reliable machine learning (ML) models, and sound decision-making. We discuss two common strategies to verify the quality of published data. The metadata of an Iceberg table stores a history of snapshots.

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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

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The Power of Graph Databases, Linked Data, and Graph Algorithms

Rocket-Powered Data Science

In their wisdom, the editors of the book decided that I wrote “too much” So, they correctly shortened my contribution by about half in the final published version of my Foreword for the book. I publish this in its original form in order to capture the essence of my point of view on the power of graph analytics.

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How companies are building sustainable AI and ML initiatives

O'Reilly on Data

In 2017, we published “ How Companies Are Putting AI to Work Through Deep Learning ,” a report based on a survey we ran aiming to help leaders better understand how organizations are applying AI through deep learning. We found companies were planning to use deep learning over the next 12-18 months.