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Practical Skills for The AI Product Manager

O'Reilly on Data

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

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Who Does the Machine Learning and Data Science Work?

Business Over Broadway

Only 1/4 of respondents said they do research to advance the state of the art of machine learning. Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals. Work Activities by Different Data Roles. Other (3%).

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Minerva – Google’s Language Model for Quantitative Reasoning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Recently, experimenters have developed a very sophisticated natural language […]. The model for natural language processing is called Minerva.

Modeling 399
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The key to operational AI: Modern data architecture

CIO Business Intelligence

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.

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Machine Learning Product Management: Lessons Learned

Domino Data Lab

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. It focuses on his ML product management insights and lessons learned. Machine Learning Projects are Hard: Shifting from a Deterministic Process to a Probabilistic One. Product Management for Machine Learning.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Two use cases illustrate how this can be applied for business intelligence (BI) and data science applications, using AWS services such as Amazon Redshift and Amazon SageMaker.

IoT 111
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The DataOps Vendor Landscape, 2021

DataKitchen

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Testing 304