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AI meets operations

O'Reilly

First, the behavior of an AI application depends on a model , which is built from source code and training data. A model isn’t source code, and it isn’t data; it’s an artifact built from the two. This means that, to have a history of how an application was developed, you have to look at more than the source code.

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Nurturing Design in Your Software Engineering Culture

Strategic Tech

There are a few qualities that differentiate average from high performing software engineering organisations. I believe that attitude towards the design of code and architecture is one of them. The same mindset should also be applied to architecture; involve the whole team and challenge the small details.

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How architecture evolves into strategy

O'Reilly Software

It's a given that we must design a system, including a local software architecture, that actually runs, that is "solid." The architect is hopefully not concerned with low-level details of the code itself inside one system but is more focused on where data center boundaries are crossed, where system component boundaries are crossed.

Strategy 100
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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

On the other hand, very few data scientists feel strongly about the nature of the data warehouse, the compute platform that trains and scores their models, or the workflow scheduler. A key observation was that most of our data scientists had nothing against writing Python code. The steps can be arbitrary Python code.

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Modelling Bounded Contexts with the Bounded Context Design Canvas: A Workshop Recipe

Strategic Tech

In Domain-Driven Design, a large system is decomposed into bounded contexts , which become natural boundaries in code as microservices and as teams in the organisation. This is the question I get asked the most, so I’ve put together this article describing a workshop recipe you can use. There is no shortcut to identifying good boundaries.

Design 76
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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

On the other hand, very few data scientists feel strongly about the nature of the data warehouse, the compute platform that trains and scores their models, or the workflow scheduler. A key observation was that most of our data scientists had nothing against writing Python code. The steps can be arbitrary Python code.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly

All ML projects are software projects. If you peek under the hood of an ML-powered application, these days you will often find a repository of Python code. If you ask an engineer to show how they operate the application in production, they will likely show containers and operational dashboards—not unlike any other software service.

DevOps 141