Track experiments, evaluate models, manage ML lifecycle, and deploy AI applications using MLflow platform
An open-source platform for the complete machine learning lifecycle, providing experiment tracking, model management, LLM observability, and deployment capabilities for AI/ML applications.
This skill helps you leverage MLflow to:
**Install MLflow:**
```bash
pip install mlflow
```
**Start the MLflow UI:**
```bash
mlflow server
```
Access the UI at `http://localhost:5000` to view experiments, traces, and evaluations.
**Enable automatic tracing for supported libraries:**
For OpenAI:
```python
import mlflow
from openai import OpenAI
mlflow.openai.autolog()
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": "Hello!"}],
temperature=0.1,
)
```
For LangChain, LlamaIndex, DSPy, or AutoGen, replace `mlflow.openai.autolog()` with the appropriate autolog function (e.g., `mlflow.langchain.autolog()`).
**View traces:**
**Run automated evaluation with built-in metrics:**
```python
import mlflow
from mlflow.genai.scorers import Correctness, Guidelines
dataset = [
{
"inputs": {"question": "What is MLflow?"},
"expectations": {"expected_response": "An ML platform"},
},
]
def predict_fn(question: str) -> str:
# Your model/LLM inference logic
return generate_response(question)
results = mlflow.genai.evaluate(
data=dataset,
predict_fn=predict_fn,
scorers=[
Correctness(),
Guidelines(name="tone_check", guidelines="Response must be professional"),
],
)
```
**Access results:**
**Enable autologging for ML frameworks:**
For scikit-learn:
```python
import mlflow
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
mlflow.sklearn.autolog()
model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)
```
**Manual logging for custom workflows:**
```python
with mlflow.start_run():
mlflow.log_param("learning_rate", 0.01)
mlflow.log_metric("accuracy", 0.95)
mlflow.log_artifact("model.pkl")
```
**Register a trained model:**
```python
with mlflow.start_run():
mlflow.sklearn.log_model(model, "random_forest_model")
model_uri = f"runs:/{run_id}/random_forest_model"
mlflow.register_model(model_uri, "ProductionModel")
```
**Manage model lifecycle:**
**Deploy as REST API:**
```bash
mlflow models serve -m models:/ProductionModel/1 -p 5001
```
**Deploy to cloud platforms:**
AWS SageMaker:
```bash
mlflow deployments create -t sagemaker -m models:/ProductionModel/1
```
Azure ML or other platforms - refer to MLflow deployment documentation for specific commands.
**Version and track prompts:**
```python
from mlflow.prompts import Prompt
prompt = Prompt(
name="customer_support_prompt",
template="You are a helpful assistant. Answer: {question}",
version="v1",
)
mlflow.log_prompt(prompt)
```
Access and reuse prompts across your organization via the MLflow UI.
MLflow natively integrates with:
**ML Frameworks:** TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, Keras, Spark MLlib
**GenAI Libraries:** OpenAI, LangChain, LlamaIndex, DSPy, AutoGen, Anthropic, Cohere
**Cloud Platforms:** AWS SageMaker, Azure ML, Databricks, Google Cloud, Nebius
**Languages:** Python, JavaScript/TypeScript, Java, R
1. **Always use autologging** when available - reduces boilerplate code
2. **Organize experiments with naming conventions** - use descriptive experiment names
3. **Tag runs with metadata** - makes filtering and comparison easier
4. **Use Model Registry for production models** - ensures proper versioning and governance
5. **Set up remote tracking server** for team collaboration - avoid local-only tracking
6. **Enable tracing early in development** - easier to debug LLM applications
7. **Create comprehensive evaluation datasets** - improves model quality assessment
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