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+# Deploy DSPy Model
+
+This guide demonstrates two popular approaches to deploy your DSPy models in production:
+
+1. FastAPI - For simple, lightweight deployments with direct model access
+2. MLflow - For production-grade deployments with model versioning and management
+
+## Deploying with FastAPI
+
+FastAPI offers a quick and straightforward way to serve your DSPy model as a REST API. This approach is ideal when you have direct access to your model code and need a lightweight deployment solution.
+
+Before we get started, let's install the required libraries:
+
+```bash
+pip install fastapi uvicorn
+```
+
+And remember to set your OpenAI API key which is used by our example:
+
+```bash
+export OPENAI_API_KEY="your-openai-api-key"
+```
+
+Here's a minimal example of a DSPy model:
+
+```python
+import dspy
+
+lm = dspy.LM("openai/gpt-4o-mini")
+dspy.settings.configure(lm=lm)
+dspy_model = dspy.ChainOfThought("question -> answer")
+```
+
+Create a FastAPI application to serve this model:
+
+```python
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+import dspy
+
+app = FastAPI(
+    title="DSPy Model API",
+    description="A simple API serving a DSPy Chain of Thought model",
+    version="1.0.0"
+)
+
+# Define request model for better documentation and validation
+class Question(BaseModel):
+    text: str
+
+# Configure your language model and Chain of Thought
+lm = dspy.LM("openai/gpt-4o-mini")
+dspy.settings.configure(lm=lm, async_max_workers=4) # default is 8
+dspy_model = dspy.asyncify(dspy.ChainOfThought("question -> answer"))
+
+@app.post("/predict")
+async def predict(question: Question):
+    try:
+        result = await dspy_model(question=question.text)
+        return {
+            "status": "success",
+            "data": result.toDict()
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+```
+
+Save your code in a file, e.g., `fastapi_dspy.py`. Then you can run the app with:
+
+```bash
+uvicorn fastapi_dspy:app --reload
+```
+
+It will start a local server at `http://127.0.0.1:8000/`. You can test it with the python code below:
+
+```python
+import requests
+
+response = requests.post(
+    "http://127.0.0.1:8000/predict",
+    json={"text": "What is the capital of France?"}
+)
+print(response.json())
+```
+
+You should see the response like below:
+
+```json
+{'status': 'success', 'data': {'reasoning': 'The capital of France is a well-known fact, commonly taught in geography classes and referenced in various contexts. Paris is recognized globally as the capital city, serving as the political, cultural, and economic center of the country.', 'answer': 'The capital of France is Paris.'}}
+```
+
+## Deploying with MLflow
+
+We recommend deploying with MLflow if you are looking for a solution to package your DSPy model and deploy in an isolated environment.
+MLflow is a popular platform for managing machine learning workflows, including model versioning, tracking, and deployment.
+
+Let's first install the required libraries:
+
+```bash
+pip install mlflow>=2.18.0
+```
+
+Let's spin up the MLflow tracking server, where we will store our DSPy model. The command below will start a local server at
+`http://127.0.0.1:5000/`.
+
+```bash
+mlflow ui
+```
+
+Then we can define the DSPy model and log it to the MLflow server. "log" is an overloaded term in MLflow, basically it means
+we store the model information along with environment requirements in the MLflow server. See the code below:
+
+```python
+import dspy
+import mlflow
+
+mlflow.set_tracking_uri("http://127.0.0.1:5000/")
+mlflow.set_experiment("deploy_dspy_model")
+
+lm = dspy.LM("openai/gpt-4o-mini")
+dspy.settings.configure(lm=lm)
+dspy_model = dspy.ChainOfThought("question -> answer")
+
+with mlflow.start_run():
+    mlflow.dspy.log_model(
+        dspy_model,
+        "dspy_model",
+        input_example={"messages": [{"role": "user", "content": "What is LLM agent?"}]},
+        task="llm/v1/chat",
+    )
+```
+
+We recommend you to set `task="llm/v1/chat"` so that the deployed model automatically takes input and generate output in
+the same format as the OpenAI chat API, which is a common interface for LLM applications nowadays. Save the code above in
+a file, e.g., `mlflow_dspy.py`, and run it.
+
+After you logged the model, you can view the saved information in MLflow UI. Open `http://127.0.0.1:5000/` and select
+the `deploy_dspy_model` experiment, then select the run your just created, under the `Artifacts` tab, you should see the
+logged model information, similar to the following screenshot:
+
+![MLflow UI](./dspy_mlflow_ui.png)
+
+Grab your run id from UI (or the console print when you execute `mlflow_dspy.py`), now you can deploy the logged model
+with the following command:
+
+```bash
+mlflow models serve -m runs:/{run_id}/model -p 6000
+```
+
+After the model is deployed, you can test it with the following command:
+
+```bash
+curl http://127.0.0.1:6000/invocations -H "Content-Type:application/json"  --data '{"messages": [{"content": "what is 2 + 2?", "role": "user"}]}'
+```
+
+You should see the response like below:
+
+```json
+{"choices": [{"index": 0, "message": {"role": "assistant", "content": "{\"reasoning\": \"The question asks for the sum of 2 and 2. To find the answer, we simply add the two numbers together: 2 + 2 = 4.\", \"answer\": \"4\"}"}, "finish_reason": "stop"}]}
+```
+
+For complete guide on how to deploy a DSPy model with MLflow, and how to customize the deployment, please refer to the
+[MLflow documentation](https://mlflow.org/docs/latest/llms/dspy/index.html).
+
+### Best Practices for MLflow Deployment
+
+1. **Environment Management**: Always specify your Python dependencies in a `conda.yaml` or `requirements.txt` file.
+2. **Model Versioning**: Use meaningful tags and descriptions for your model versions.
+3. **Input Validation**: Define clear input schemas and examples.
+4. **Monitoring**: Set up proper logging and monitoring for production deployments.
+
+For production deployments, consider using MLflow with containerization:
+
+```bash
+mlflow models build-docker -m "runs:/{run_id}/model" -n "dspy-model"
+docker run -p 6000:8080 dspy-model
+```
+
+For a complete guide on production deployment options and best practices, refer to the
+[MLflow documentation](https://mlflow.org/docs/latest/llms/dspy/index.html).
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