ai-pipeline-orchestration

name	ai-pipeline-orchestration
description	Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI systems.
license	MIT
metadata	{"author":"devops-skills","version":"1.0"}

AI Pipeline Orchestration

Build reliable, observable AI workflows — from document ingestion to batch inference to model training pipelines.

When to Use This Skill

Use this skill when:

• Scheduling recurring RAG document ingestion and re-indexing
• Orchestrating multi-step batch LLM processing workflows
• Running nightly model evaluation and fine-tuning jobs
• Building ETL pipelines that feed into AI models
• Managing dependencies between data preparation and model serving

Tool Selection

Tool	Best For	Complexity	GPU Jobs
Prefect	Modern Python-first; easy to adopt	Low	Good
Airflow	Complex DAGs; large teams; existing usage	High	Good
Dagster	Asset-centric; strong data lineage	Medium	Excellent
Temporal	Long-running workflows; reliability-first	Medium	Good

Prefect — Quick Start

pip install prefect prefect-kubernetes

# Start Prefect server (or use Prefect Cloud)
prefect server start

# In another terminal
prefect worker start --pool default-agent-pool

Prefect: RAG Ingestion Pipeline

from prefect import flow, task, get_run_logger
from prefect.tasks import task_input_hash
from datetime import timedelta
import hashlib

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(hours=24))
def fetch_documents(source_url: str) -> list[dict]:
    """Fetch documents from source; cached to avoid re-fetching."""
    logger = get_run_logger()
    logger.info(f"Fetching from {source_url}")
    # ... fetch logic
    return documents

@task(retries=3, retry_delay_seconds=30)
def chunk_and_embed(documents: list[dict]) -> list[dict]:
    """Chunk documents and generate embeddings with retry on failure."""
    from sentence_transformers import SentenceTransformer
    model = SentenceTransformer("BAAI/bge-large-en-v1.5")
    chunks = []
    for doc in documents:
        doc_chunks = chunk_text(doc["content"])
        embeddings = model.encode(doc_chunks, batch_size=64)
        for chunk, emb in zip(doc_chunks, embeddings):
            chunks.append({"text": chunk, "embedding": emb.tolist(),
                           : doc[], : doc[]})
     chunks


 () -> :
    
     qdrant_client  QdrantClient
    client = QdrantClient()
    client.upsert(collection_name=, points=[...])
     (chunks)


 ():
    
    logger = get_run_logger()
    total = 
     source  sources:
        docs = fetch_documents(source)
        chunks = chunk_and_embed(docs)
        count = upsert_to_vector_store(chunks)
        total += count
        logger.info()
    logger.info()

 __name__ == :
    rag_ingestion_pipeline.serve(
        name=,
        cron=,          
        parameters={: [, ]},
    )

Prefect: Batch LLM Inference Pipeline

from prefect import flow, task
from prefect.concurrency.sync import concurrency
import asyncio
from openai import AsyncOpenAI

@task(retries=3, retry_delay_seconds=60)
async def process_batch(items: list[dict], model: str = "gpt-4o-mini") -> list[dict]:
    """Process a batch of items through LLM with rate limit protection."""
    client = AsyncOpenAI()
    async with concurrency("openai-api", occupy=len(items)):  # rate limit
        tasks = [
            client.chat.completions.create(
                model=model,
                messages=[{"role": "user", "content": item["prompt"]}],
                max_tokens=256,
            )
            for item in items
        ]
        responses = await asyncio.gather(*tasks, return_exceptions=True)

    results = []
    for item, response in zip(items, responses):
        if isinstance(response, Exception):
            results.append({**item, "error": str(response), "output": None})
        else:
            results.append({**item, : response.choices[].message.content})
     results


  ():
     json
    items = [json.loads(line)  line  (input_file)]
    batches = [items[i:i+batch_size]  i  (, (items), batch_size)]

    all_results = []
     batch  batches:
        results =  process_batch(batch)
        all_results.extend(results)

     (output_file, )  f:
         result  all_results:
            f.write(json.dumps(result) + )
     (all_results)

Airflow: Model Training DAG

from airflow.decorators import dag, task
from airflow.providers.cncf.kubernetes.operators.pod import KubernetesPodOperator
from datetime import datetime
from kubernetes.client import models as k8s

@dag(
    dag_id="llm_fine_tuning",
    schedule="@weekly",
    start_date=datetime(2025, 1, 1),
    catchup=False,
    tags=["ai", "training"],
)
def llm_fine_tuning_dag():

    @task
    def prepare_dataset() -> str:
        """Download and preprocess training data."""
        # ... data prep logic
        return "s3://my-bucket/training-data/2025-03-01/"

    train = KubernetesPodOperator(
        task_id="train_model",
        name="llm-training-job",
        namespace="ml",
        image="nvcr.io/nvidia/pytorch:24.05-py3",
        cmds=["accelerate", "launch", "-m", "axolotl.cli.train", "/config/config.yaml"],
        resources=k8s.V1ResourceRequirements(
            limits={"nvidia.com/gpu": "4", "memory": "320Gi"},
            requests={"nvidia.com/gpu": "4"},
        ),
        node_selector={"nvidia.com/gpu.product": },
        volumes=[...],
        volume_mounts=[...],
        get_logs=,
        is_delete_operator_pod=,
    )


     () -> :
        
        metrics = run_evals()
         metrics[] < :
             ValueError()
         metrics


     ():
        
        update_serving_config(new_model=)

    dataset = prepare_dataset()
    train.set_upstream(dataset)
    eval_result = evaluate_model(dataset)
    eval_result.set_upstream(train)
    deploy_model(eval_result)

llm_fine_tuning_dag()

Dagster: Asset-Based AI Pipeline

from dagster import asset, AssetExecutionContext, define_asset_job, ScheduleDefinition

@asset(description="Raw documents fetched from knowledge sources")
def raw_documents(context: AssetExecutionContext) -> list[dict]:
    context.log.info("Fetching documents...")
    return fetch_all_documents()

@asset(
    deps=[raw_documents],
    description="Chunked and embedded document vectors",
)
def document_embeddings(context: AssetExecutionContext, raw_documents) -> int:
    chunks = process_and_embed(raw_documents)
    context.log.info(f"Generated {len(chunks)} embeddings")
    upsert_to_qdrant(chunks)
    return len(chunks)

@asset(
    deps=[document_embeddings],
    description="RAG system quality metrics",
)
def rag_quality_metrics(context: AssetExecutionContext) -> dict:
    metrics = evaluate_rag_system()
    context.add_output_metadata({"ragas_score": metrics["ragas_score"]})
    return metrics

# Schedule: refresh embeddings nightly
nightly_refresh = ScheduleDefinition(
    job=define_asset_job("rag_refresh_job", [raw_documents, document_embeddings]),
    cron_schedule="0 1 * * *",
)

Best Practices

• Use task-level retries for API calls; use flow-level retries for transient infra failures.
• Cache expensive steps (embedding generation, data fetching) to speed up reruns.
• Emit custom metrics from pipelines (chunk count, error rate, cost) to your observability stack.
• Use concurrency limits in Prefect or pool slots in Airflow to respect external rate limits.
• Separate ingestion, training, and deployment pipelines — don't couple them in one giant DAG.

Related Skills

• rag-infrastructure - RAG system setup
• llm-fine-tuning - Training jobs
• agent-observability - Pipeline monitoring
• kubernetes-ops - Running pipeline pods on K8s