MLOps for Local AI

07. Pipeline Orchestration

Chapter 7 of 24 · 15 min

KEY INSIGHT

Orchestration introduces operational complexity. Start with manual scripts; graduate to orchestration when the overhead of automation (configuration, monitoring, debugging) is less than the cost of manual errors. The core concepts: - **Task**: A single unit of work (run a script, call an API) - **DAG**: The graph defining task dependencies - **Trigger**: What initiates the pipeline (schedule, event, manual) - **Executor**: Where and how tasks run (local, Kubernetes, Ray) - **Operator**: The interface defining a task type ML-specific patterns: ```python # Pseudo-code for an ML pipeline pipeline: - task: fetch_data trigger: schedule "@daily" - task: validate_data depends: [fetch_data] condition: "data quality score > 0.95" - task: train_model depends: [validate_data] resources: gpu=true, memory=16Gi - task: evaluate_model depends: [train_model] condition: "accuracy > 0.90 AND latency < 100ms" - task: deploy_model depends: [evaluate_model] approval: required ``` Conditional execution is critical. You don't deploy models that fail validation. You don't retrain if data hasn't changed. Orchestration makes these conditions explicit and automated.

Pipeline orchestration transforms ad-hoc training scripts into automated, reproducible workflows. Instead of "run this script when you remember," you define pipelines that trigger on schedule, data arrival, or upstream completion.

A pipeline is a directed acyclic graph (DAG) of tasks. Each task is a discrete unit: fetch data, validate data, train model, evaluate model, deploy model. Orchestrators handle execution order, failure recovery, and logging.

Why orchestrate ML pipelines specifically? ML pipelines have unique characteristics: data-dependent execution times, resource-intensive training steps, evaluation gates that can halt progression, and retraining triggered by drift detection.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

EXERCISE

Identify three training workflows you currently run manually. For each, document: inputs, outputs, execution time, failure modes, and dependencies. This becomes your candidate for orchestration.