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Deployment is the process of releasing software into the environment where users will actually use it: the passage from code that works on the developer's machine to a service that works in production. It is every project's moment of truth, and modern engineering has turned it from a rare, risky event into a daily, reversible routine.
CI/CD pipelines (Continuous Integration / Continuous Deployment) automate the journey: on every change the code is built, tested and packaged, then rolled out with risk-reducing strategies: blue-green (two identical environments, traffic switches in one move), canary (the novelty goes first to a small percentage of users, like the canary in the mine), rolling (gradual node-by-node update). If metrics degrade, a rollback restores the previous version.
In AI, deployment has its own nuances: you release models as well as code (with A/B tests between versions and shadow deployments, where the new model receives real traffic without answering users) and even prompts follow versioning and release cycles. The discipline that industrializes all this is called MLOps. Beyond AI, web giants perform thousands of deployments a day.
The term comes from military vocabulary: to deploy is to position troops in the field, from the French déployer, to unfold (in turn from the Latin displicare). Software inherited the idea of putting things into operational position. Continuous release practice was codified between 2009 and 2010, with the birth of the DevOps movement and Jez Humble and David Farley's book Continuous Delivery (2010).
The Grace certification exam is your personal deployment: the skills trained in the gym go into production, with a verifiable URL anyone can consult. The professional scenarios on MLOps train you to ask the AI for gradual release, canary and rollback strategies for models and prompts.
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