Google Cloud ML Engineer: Deployment and Monitoring That Survive Production
A model can perform well in a notebook and fail within its first week as a service. The difficult work starts when…
Read articleNew thinking and practical technical perspectives
A model can perform well in a notebook and fail within its first week as a service. The difficult work starts when…
Read articleTraining a model is more than starting a compute job and waiting for a success message. A production-grade training design controls data…
Read articleA service organization can have talented engineers, dependable technology and detailed procedures while still making inconsistent decisions about value and risk. Governance…
Read articleA value stream is not a list of departments or a diagram of every software ticket. It is the connected work through…
Read articleModern service management becomes much easier to understand when a team stops treating a service as a queue of tickets and starts…
Read articleContinual improvement is not an annual exercise in setting new targets. It is the ability to recognize a service problem, identify what…
Read articleThe ITIL 4 guiding principles are useful precisely because they do not prescribe one fixed process for every organization. They help people…
Read articleThe ITIL 4 Service Value System explains how an organization turns opportunities and demand into value through coordinated service management. It is…
Read articleA release strategy is an answer to two questions: how will users encounter the new version, and what happens if it behaves…
Read articleVersion control is not just a place to store code. Repository structure, branch permissions, review rules and automation triggers determine how software…
Read articleA build pipeline can be green while the release is unsafe. Source code may compile and unit tests may pass, yet the…
Read articleA Databricks notebook that works in one engineer’s development workspace is not automatically a production data product. It may depend on a…
Read article