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The road to Software 2.0

O'Reilly on Data

Roughly a year ago, we wrote “ What machine learning means for software development.” In that article, we talked about Andrej Karpathy’s concept of Software 2.0. Karpathy argues that we’re at the beginning of a profound change in the way software is developed. Instead, we can program by example.

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Managing risk in machine learning

O'Reilly on Data

As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Let’s begin by looking at the state of adoption.

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Deep automation in machine learning

O'Reilly on Data

In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. Humans are still needed to write software, but that software is of a different type. Developers of Software 1.0

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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. Image by Matei Zaharia; used with permission.

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Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. Machine learning developers are beginning to look at an even broader set of risk factors. Sources of model risk.

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Oracle Wants to Be the Database for AI

David Menninger's Analyst Perspectives

Founded as Software Development Laboratories in 1977, Oracle is a behemoth in the software industry, generating more than $50 billion in revenue in its fiscal year 2024. Originally focused solely on the relational database market, the software provider operated as Relational Systems, Inc.

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