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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