MLflow ML Lifecycle Management

An open-source platform for the complete machine learning lifecycle, providing experiment tracking, model management, LLM observability, and deployment capabilities for AI/ML applications.

What This Skill Does

This skill helps you leverage MLflow to:

Instructions

1. Installation and Setup

**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.

2. LLM Tracing and Observability

**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:**

3. Evaluating LLMs and AI Applications

**Run automated evaluation with built-in metrics:**

```python

import mlflow

from mlflow.genai.scorers import Correctness, Guidelines

Define evaluation dataset

dataset = [

{

"inputs": {"question": "What is MLflow?"},

"expectations": {"expected_response": "An ML platform"},

},

]

Define prediction function

def predict_fn(question: str) -> str:

# Your model/LLM inference logic

return generate_response(question)

Run evaluation

results = mlflow.genai.evaluate(

data=dataset,

predict_fn=predict_fn,

scorers=[

Correctness(),

Guidelines(name="tone_check", guidelines="Response must be professional"),

],

)

```

**Access results:**

4. Experiment Tracking for Model Training

**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()

Train model - MLflow automatically logs params, metrics, model

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")

```

5. Model Registry and Versioning

**Register a trained model:**

```python

Log model during training

with mlflow.start_run():

mlflow.sklearn.log_model(model, "random_forest_model")

Register to Model Registry

model_uri = f"runs:/{run_id}/random_forest_model"

mlflow.register_model(model_uri, "ProductionModel")

```

**Manage model lifecycle:**

6. Model Deployment

**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.

7. Prompt Management (GenAI)

**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.

Integration Support

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

Key Constraints

Best Practices

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

Resources