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(), "source": doc["url"], "doc_hash": doc["hash"]}) return chunks

@task(retries=2) def upsert_to_vector_store(chunks: list[dict]) -> int: """Upsert embeddings to Qdrant, skip unchanged documents.""" from qdrant_client import QdrantClient client = QdrantClient("http://qdrant:6333") client.upsert(collection_name="knowledge-base", points=[...]) return len(chunks)

@flow(name="rag-ingestion", log_prints=True) def rag_ingestion_pipeline(sources: list[str]): """Full RAG ingestion flow — runs daily.""" logger = get_run_logger() total = 0 for source in sources: docs = fetch_documents(source) chunks = chunk_and_embed(docs) count = upsert_to_vector_store(chunks) total += count logger.info(f"Ingested {count} chunks from {source}") logger.info(f"Pipeline complete: {total} total chunks indexed")

if name == "main": rag_ingestion_pipeline.serve( name="daily-rag-ingestion", cron="0 2 * * *", # 2 AM daily parameters={"sources": ["https://docs.myapp.com", "https://api.myapp.com/kb"]}, )

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, "output": response.choices[0].message.content})
return results

@flow(name="batch-llm-inference") async def batch_inference_flow(input_file: str, output_file: str, batch_size: int = 50): import json items = [json.loads(line) for line in open(input_file)] batches = [items[i:i+batch_size] for i in range(0, len(items), batch_size)]

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

with open(output_file, "w") as f:
    for result in all_results:
        f.write(json.dumps(result) + "\n")
return len(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": "A100-SXM4-80GB"},
    volumes=[...],
    volume_mounts=[...],
    get_logs=True,
    is_delete_operator_pod=True,
)

@task
def evaluate_model(dataset_path: str) -> dict:
    """Run evals; fail pipeline if quality drops."""
    metrics = run_evals()
    if metrics["accuracy"] < 0.85:
        raise ValueError(f"Model quality too low: {metrics}")
    return metrics

@task
def deploy_model(metrics: dict):
    """Push merged model to HF Hub and update vLLM config."""
    update_serving_config(new_model="org/fine-tuned-v2")

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