Vertex AI Pipelines orchestrate multi-step machine learning workflows so you can move from raw data to a deployed model in a repeatable, debuggable way. In simple terms, a pipeline is a directed graph of steps—data prep, training, evaluation, registration, deployment—each running in an isolated, containerized component. Pipelines track inputs, outputs, and metadata for every step, which gives you lineage (what produced what), caching (skip unchanged work), and reproducibility (re-run with the same artifacts). This turns a fragile set of scripts into a production-grade process you can schedule, parameterize, and audit.
Practically, each step mounts data from Cloud Storage or BigQuery, executes your logic (e.g., a Python component or container), and emits artifacts like datasets, metrics, and model binaries. Vertex AI stores these artifacts and their lineage in the control plane, so when you change a parameter or new data lands, only the affected steps re-run. You can add conditional branches: for example, if evaluation AUC falls below a threshold, you abort instead of deploying. For governance, pipeline runs serve as your “paper trail,” showing exactly which data, code SHA, and hyperparameters created the model that’s now serving traffic.
Pipelines also coordinate vector-centric tasks. One path might generate embeddings in batch, write them to Cloud Storage, and upsert them into Milvus; another trains a ranker or generator used at query time. During model upgrades, a pipeline can materialize a parallel Milvus collection with the new embeddings, run shadow evaluations (recall@k, MRR, latency), and only then cut traffic over. This gives you safe, automated iteration cycles for retrieval-augmented generation (RAG), semantic search, and agent memory—where data freshness, index health, and model quality must evolve together without downtime.