Design and Implement a GenAIOps Infrastructure
GenAIOps is MLOps with nondeterministic artifacts: prompts, indexes, model deployments, and evaluation suites all need versioning and promotion. Think of the pipeline as ideate → experiment → evaluate → deploy → monitor → iterate.
Core assets. Prompt assets as code live in a repo or a flow definition so every change is diffable, reviewable, and reversible. Connections in the AI platform abstract credentials to Azure OpenAI, Azure AI Search, or storage; the connection holds the endpoint and references a Key Vault secret, and the app authenticates with managed identity. Indexes built from your documents are infrastructure too - version the chunking and embedding model alongside them, because re-embedding with a new model invalidates the index. Model deployments have types: standard (pay per token, shared capacity) vs. provisioned throughput (reserved capacity for predictable latency) - a scenario mentioning steady high volume and latency SLAs points to provisioned.
CI/CD for GenAI adds steps that classic ML does not: run the evaluation suite against a golden dataset, run content-safety checks on both inputs and outputs, and compare metrics to a baseline before promoting a prompt or flow. Include retry, timeout, and fallback (secondary region or model) handling in the flow itself.
Traps. Confusing a connection (endpoint + credential reference) with a secret (the key itself). Promoting a prompt without re-running evaluations. Forgetting that evaluation requires an evaluator model deployment and consumes tokens and quota. Assuming the RAG index auto-refreshes when source documents change.
Rule of thumb: anything that changes model behavior - prompt text, system message, temperature, retrieval config, index - is a release and needs a version, a test run, and a rollback path.