Data Science and Standard Patterns

In Data Science, the word “Pattern” has a specific meaning, involving the patterns that arise from data. This type of analysis is quite common in Data Mining and other technologies used by a Data Scientist. In IT practices such as systems architecture and software solutions design, the word “Pattern” has another definition, which is to…

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Analytics is Width. Feature Selection is Depth.

Most organizations don’t focus on Data Science or AI or Machine Learning as a single discipline – they group it together with the entire Analytics function. This includes everything from spreadsheets to Relation data, from documents stored in multiple locations to the structured business data in standard operations. While you might view your team independently,…

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DevOps for Data Science – Load Testing and Auto-Scale

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. You can find each Maturity Model article in the series here: Infrastructure as Code (IaC)…

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DevOps for Data Science – Application Performance Monitoring

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects is to implement Infrastructure as Code…

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DevOps for Data Science – Release Management

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects is to implement Infrastructure as Code…

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DevOps for Data Science – Continuous Delivery

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects is to implement Infrastructure as Code…

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DevOps for Data Science – Automated Testing

I have a series of posts on DevOps for Data Science where I am covering a set of concepts for a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. In this article, I’ll cover the next maturity…

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DevOps for Data Science – Continuous Integration

In the previous post in this series on DevOps for Data Science, I covered the first the concept in a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects…

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DevOps for Data Science – Infrastructure as Code

In the previous post in this series on DevOps for Data Science, I explained that it’s often difficult to try and implement all of the DevOps practices and tools at one time. I introduced the concept of a “Maturity Model” – a list of things you can do, in order, that will set you on…

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DevOps for Data Science – DevOps Maturity

In this series on DevOps for Data Science, I’ve explained what DevOps is, and given you lots of resources to go learn more about it. Now we can get to the details of implementing DevOps in your Data Science Projects. Consider that the standard Software Development Lifecycle (SDLC) with Data Science algorithms or API’s added…

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