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Specialized tools for machine learning development and model governance are becoming essential

O'Reilly on Data

Why companies are turning to specialized machine learning tools like MLflow. A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. The upcoming 0.9.0

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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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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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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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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Improve accuracy and resiliency of analytics and machine learning by fostering data standards and high-quality data products. In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications. This process is shown in the following figure.

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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.

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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.