# Model serving with FastAPI
from fastapi import FastAPI
import torch

app = FastAPI()
model = torch.load("model.pth")

@app.post("/predict")
async def predict(data: dict):
    tensor = preprocess(data)
    with torch.no_grad():
        prediction = model(tensor)
    return {"prediction": prediction.tolist()}

# Feast feature store
from feast import FeatureStore

store = FeatureStore(repo_path=".")
features = store.get_online_features(
    features=["user_features:age", "user_features:location"],
    entity_rows=[{"user_id": 123}]
).to_dict()

# Drift detection
from evidently import ColumnMapping
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset

report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=ref_df, current_data=curr_df)

# Basic RAG with LangChain
from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA

vectorstore = Pinecone.from_existing_index(
    index_name="docs",
    embedding=OpenAIEmbeddings()
)
qa = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever()
)

# Structured prompts with DSPy
import dspy

class QA(dspy.Signature):
    """Answer questions based on context."""
    context = dspy.InputField()
    question = dspy.InputField()
    answer = dspy.OutputField()

qa = dspy.Predict(QA)

# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/

# Training
python scripts/train.py --config prod.yaml
mlflow run . -P epochs=10

# Deployment
docker build -t model:v1 .
kubectl apply -f k8s/model-serving.yaml

# Monitoring
mlflow ui --port 5000