Build MLOps pipelines for training, evaluation, and deployment. Reproducibility and monitoring.
MLOps bridges experimentation and production. Here’s how to run reproducible training and deployment pipelines.
Start with a simple pipeline (train → eval → deploy) and add monitoring and automation as usage grows.
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We ran Istio for a year, then switched to Linkerd. Both can do the job. The decision came down to operational fit, not features.
We've had to restore a Kubernetes cluster from backup twice. Once it worked. Once it took 14 hours. Here's the strategy we run now.
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AI apps add a new attack surface on top of the old ones. This is the map: the threats unique to LLMs and agents, and the controls that actually contain them.
You cannot prove an LLM app is safe by reading its prompt. Here is how to adversarially test it before attackers do.
Autonomous agents take real actions, so a single injected instruction can cause real damage. Here is how to contain them.
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