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Bringing an AI Product to Market

O'Reilly on Data

In this article, we turn our attention to the process itself: how do you bring a product to market? Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business. Identifying the problem.

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6 trends framing the state of AI and ML

O'Reilly on Data

Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%. Deep learning cooled slightly in 2019, slipping 10% relative to 2018, but deep learning still accounted for 22% of all AI/ML usage.

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How Do Super Rookies Start Learning Data Analysis?

FineReport

For newbies, a tool with low learning difficulty but powerful analytical performance cannot be better. In addition, Jupyter Notebook is also an excellent interactive tool for data analysis and provides a convenient experimental platform for beginners. What Data Analysis Tools Should I Learn to Start a Career as a Data Analyst?

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. It analyzes historical data and news articles, confirming a possible market correction.

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The New Improved and Open GraphDB

Ontotext

This new version sees the release of essential plug-ins: data virtualization, a sophisticated ranking of semantic search results, new types of indexing and advanced graph analytics. GraphDB has always been compliant with W3C standards and an active member of the semantic web community. The Plugins.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles. 2) MLOps became the expected norm in machine learning and data science projects. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. ” BTW, that Knuth article from 1983 was probably the first time that I ever saw the word “Web” used as a computer-related meaning. Introduction. BTW, videos for Rev2 are up: [link]. Software writes Software?

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