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The key to operational AI: Modern data architecture

CIO Business Intelligence

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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AI adoption in the enterprise 2020

O'Reilly on Data

More than half of respondent organizations identify as “mature” adopters of AI technologies: that is, they’re using AI for analysis or in production. Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

These data-fueled innovations come in the form of new algorithms, new technologies, new applications, new concepts, and even some “old things made new again”. 1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

In this article, we want to dig deeper into the fundamentals of machine learning as an engineering discipline and outline answers to key questions: Why does ML need special treatment in the first place? What does a modern technology stack for streamlined ML processes look like? Can’t we just fold it into existing DevOps best practices?

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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Managing Machine Learning Projects” (AWS). People + AI Guidebook” (Google).

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The DataOps Vendor Landscape, 2021

DataKitchen

Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Saagie — Seamlessly orchestrates big data technologies to automate analytics workflows and deploy business apps anywhere. . Polyaxon — An open-source platform for reproducible machine learning at scale.

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Enterprise Data Science Workflows with AMPs and Streamlit

Cloudera

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. We believe the best way to learn what a technology is capable of is to build things with it.