Syllabuck: Ignite 2018 Conference
(A “Syllabuck” is like a Syllabus, but more like that second definition, and certainly more random) I recently attended, presented, …
Category Archives: Data Science, Artificial Intelligence and Advanced Analytics Project Management
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 …
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 …
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 …
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 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 …
DevOps for Data Science – DevOps Maturity
In this series on DevOps for Data Science, I explained what DevOps is, and given you lots of resources to go …
DevOps for Data Science – Defining DevOps
I’m wading into treacherous waters here in my series on DevOps for Data Science. Computing terms often defy explanation, especially …
The Keys to Effective Data Science Projects – Part 9: Testing and Validation
We’re continuing our discussion of the series of the Keys to Effective Data Science Projects, this time focusing on Testing and Validating …
The Keys to Effective Data Science Projects – Part 8: Operationalize
We’re in part eight on our journey through the series of the Keys to Effective Data Science Projects –“Operationalization” – a …
