Community Model Flavors

MLflow's vibrant community has developed flavors for specialized ML frameworks and use cases, extending MLflow's capabilities beyond the built-in flavors. These community-maintained packages enable seamless integration with domain-specific tools for time series forecasting, anomaly detection, visualization, and more.

Quick Start

Installing Community Flavors

Most community flavors are available via PyPI:

# Time series forecasting
pip install mlflow[sktime]
pip install mlflavors

# Visualization and plotting
pip install mlflow-vizmod

# Big data and cloud platforms
pip install bigmlflow
pip install mlflow[aliyun-oss]

Basic Usage Pattern

All community flavors follow MLflow's standard interface:

import mlflow
import community_flavor  # Replace with actual flavor

# Train your model
model = SomeModel()
model.fit(data)

# Log with MLflow
with mlflow.start_run():
    community_flavor.log_model(model, "model_path")

# Load for inference
loaded_model = community_flavor.load_model("model_uri")
predictions = loaded_model.predict(new_data)

Featured Community Flavors

Time Series

Sktime

Unified interface for time series forecasting, classification, and transformation.

pip install sktime[mlflow]

import pandas as pd
from sktime.datasets import load_airline
from sktime.forecasting.arima import AutoARIMA
from sktime.utils import mlflow_sktime

# Load data and train model
airline = load_airline()
model = AutoARIMA(sp=12, d=0, max_p=2, max_q=2, suppress_warnings=True)
model.fit(airline, fh=[1, 2, 3])

# Save and load with MLflow
mlflow_sktime.save_model(sktime_model=model, path="model")
loaded_model = mlflow_sktime.load_model(model_uri="model")

# Make predictions
predictions = loaded_model.predict()
print(predictions)

# Load as PyFunc for serving
loaded_pyfunc = mlflow_sktime.pyfunc.load_model(model_uri="model")
pyfunc_predictions = loaded_pyfunc.predict(pd.DataFrame())

MLflavors Package

Support for multiple time series and ML frameworks in one package.

pip install mlflavors

Supported Frameworks:

Framework	Category	Example Use Case
Orbit	Time Series	Bayesian forecasting
StatsForecast	Time Series	Statistical models
PyOD	Anomaly Detection	Outlier detection
SDV	Synthetic Data	Privacy-preserving data generation

PyOD Anomaly Detection Example:

import mlflow
from pyod.models.knn import KNN
from pyod.utils.data import generate_data
import mlflavors

# Generate synthetic data
contamination = 0.1
n_train, n_test = 200, 100
X_train, X_test, _, y_test = generate_data(
    n_train=n_train, n_test=n_test, contamination=contamination
)

with mlflow.start_run():
    # Train KNN detector
    clf = KNN()
    clf.fit(X_train)

    # Log model
    mlflavors.pyod.log_model(
        pyod_model=clf, artifact_path="anomaly_detector", serialization_format="pickle"
    )

    # Evaluate
    scores = clf.decision_function(X_test)
    mlflow.log_metric("mean_anomaly_score", scores.mean())

Serving PyOD Model:

# Load as PyFunc
loaded_pyfunc = mlflavors.pyod.pyfunc.load_model(model_uri="model_uri")

# Create configuration for inference
import pandas as pd

predict_conf = pd.DataFrame([{"X": X_test, "predict_method": "decision_function"}])

anomaly_scores = loaded_pyfunc.predict(predict_conf)[0]

Visualization

MLflow VizMod

Treat visualizations as models for versioning, tracking, and deployment.

pip install mlflow-vizmod

Create and Log Interactive Visualizations:

from sklearn.datasets import load_iris
import altair as alt
import mlflow_vismod

# Load data
df_iris = load_iris(as_frame=True)

# Create Altair visualization
viz_iris = (
    alt.Chart(df_iris)
    .mark_circle(size=60)
    .encode(x="sepal_length:Q", y="sepal_width:Q", color="target:N")
    .properties(height=375, width=575)
    .interactive()
)

# Log visualization as a model
with mlflow.start_run():
    mlflow_vismod.log_model(
        model=viz_iris,
        artifact_path="iris_viz",
        style="vegalite",
        input_example=df_iris.head(5),
    )

Benefits: Version Control: Track visualization changes over time. Reproducibility: Recreate exact visualizations with same data. Deployment: Serve interactive visualizations as web services. Collaboration: Share visualizations with consistent metadata.

Big Data & Cloud

BigML Integration

Deploy and manage BigML supervised models through MLflow.

pip install bigmlflow

import json
import mlflow
import bigmlflow

# Load BigML model from JSON
MODEL_FILE = "logistic_regression.json"
with mlflow.start_run():
    with open(MODEL_FILE) as handler:
        model = json.load(handler)

    # Log BigML model
    bigmlflow.log_model(
        model,
        artifact_path="bigml_model",
        registered_model_name="production_classifier",
    )

# Load and use for inference
loaded_model = bigmlflow.load_model("model_uri")
predictions = loaded_model.predict(test_dataframe)

# Load as PyFunc
pyfunc_model = mlflow.pyfunc.load_model("model_uri")
pyfunc_predictions = pyfunc_model.predict(test_dataframe)

Key Features: BigML Integration: Direct support for BigML's supervised models. PyFunc Compatible: Use with MLflow's generic Python function interface. Model Registry: Register BigML models for production deployment. DataFrame Inference: Standard pandas DataFrame input/output.

Mlflow Go

MLflow Go Backend

High-performance Go implementation of the MLflow tracking server for better scalability and performance.

pip install mlflow-go-backend

Performance Benefits:

• Faster API calls for critical tracking operations
• Higher concurrency - handles more simultaneous requests
• Improved throughput for high-volume ML workloads
• Drop-in replacement for existing MLflow deployments

Server Usage

Replace your existing MLflow server command:

# Traditional MLflow server
mlflow server --backend-store-uri postgresql://user:pass@localhost:5432/mlflow

# High-performance Go backend
mlflow-go server --backend-store-uri postgresql://user:pass@localhost:5432/mlflow

All existing MLflow server options are supported:

mlflow-go server \
  --backend-store-uri postgresql://user:pass@localhost:5432/mlflow \
  --artifacts-destination s3://my-mlflow-artifacts \
  --host 0.0.0.0 \
  --port 5000 \
  --workers 4

Client Usage

Enable Go backend in your Python code:

import mlflow
import mlflow_go_backend

# Enable the Go client implementation
mlflow_go_backend.enable_go()

# Set tracking URI (database required)
mlflow.set_tracking_uri("postgresql://user:pass@localhost:5432/mlflow")

# Use MLflow as normal - all operations now use Go backend
mlflow.set_experiment("high-performance-experiment")

with mlflow.start_run():
    mlflow.log_param("algorithm", "xgboost")
    mlflow.log_metric("accuracy", 0.95)
    mlflow.log_artifact("model.pkl")

Direct Store Usage

Use Go backend directly with MLflow stores:

import mlflow
import mlflow_go_backend

# Enable the Go client implementation (disabled by default)
mlflow_go_backend.enable_go()

# Set the tracking URI (you can also set it via the environment variable MLFLOW_TRACKING_URI)
# Currently only database URIs are supported
mlflow.set_tracking_uri("sqlite:///mlflow.db")

# Use MLflow as usual
mlflow.set_experiment("my-experiment")

with mlflow.start_run():
    mlflow.log_param("param", 1)
    mlflow.log_metric("metric", 2)

Performance Benchmarks

Initial benchmarks show significant performance improvements:

API Response Times:

• Search Runs: 60% faster than Python implementation
• Log Metrics: 45% faster batch logging
• Get Experiment: 70% faster retrieval

Concurrency:

• 2x more concurrent requests supported
• Better resource utilization under load
• Reduced memory footprint for server operations

Requirements & Limitations

Database Required: The Go backend currently requires a database backend store. File-based storage is not supported.

# Supported database URIs
mlflow.set_tracking_uri("sqlite:///mlflow.db")
mlflow.set_tracking_uri("postgresql://user:pass@host:5432/db")
mlflow.set_tracking_uri("mysql://user:pass@host:3306/db")

# Not supported yet
# mlflow.set_tracking_uri("file:///local/mlruns")

Endpoint Coverage: Most MLflow REST API endpoints are implemented in Go. Missing endpoints automatically fall back to the Python implementation for seamless compatibility.

Migration Guide

Step 1: Install Go Backend

pip install mlflow-go-backend

Step 2: Update Server Command

# Old command
mlflow server --backend-store-uri postgresql://...

# New command
mlflow-go server --backend-store-uri postgresql://...

Step 3: Enable in Client Code

import mlflow_go_backend

mlflow_go_backend.enable_go()

Step 4: Verify Performance

import time
import mlflow

# Benchmark your workload
start_time = time.time()
with mlflow.start_run():
    for i in range(1000):
        mlflow.log_metric(f"metric_{i}", i)
duration = time.time() - start_time
print(f"Logged 1000 metrics in {duration:.2f} seconds")

Contributing to Go Backend

The MLflow Go Backend is actively seeking contributors:

Missing Endpoints: Help implement remaining REST API endpoints in Go Performance Optimization: Improve existing Go implementations Testing: Add test coverage for edge cases and performance scenarios Documentation: Improve setup and usage documentation

Getting Started with Go: If you're new to Go, the project maintainers have compiled learning resources to help you contribute effectively.

Community Support: Join the #mlflow-go channel on the official MLflow Slack for questions and collaboration.

Custom Flavors

Creating Your Own Flavor

Build custom flavors for specialized ML frameworks not covered by existing options.

Flavor Structure Requirements

Every custom flavor must implement these core functions:

# Required functions for any custom flavor
def save_model(model, path, **kwargs):
    """Save model to specified path with MLflow format"""
    pass


def log_model(model, artifact_path, **kwargs):
    """Log model to current MLflow run"""
    pass


def load_model(model_uri):
    """Load model from MLflow format"""
    pass


def _load_pyfunc(path):
    """Load model as PyFunc for generic inference"""
    pass

Example: Custom Sktime Flavor Implementation

import os
import pickle
import mlflow
from mlflow import pyfunc
from mlflow.models import Model
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _PYTHON_ENV_FILE_NAME

FLAVOR_NAME = "sktime"
SERIALIZATION_FORMAT_PICKLE = "pickle"


def save_model(
    sktime_model, path, conda_env=None, serialization_format=SERIALIZATION_FORMAT_PICKLE
):
    """Save sktime model in MLflow format"""

    # Validate and prepare save path
    os.makedirs(path, exist_ok=True)

    # Create MLflow model configuration
    mlflow_model = Model()

    # Save the actual model
    model_data_subpath = "model.pkl"
    model_data_path = os.path.join(path, model_data_subpath)

    with open(model_data_path, "wb") as f:
        pickle.dump(sktime_model, f)

    # Add PyFunc flavor for generic inference
    pyfunc.add_to_model(
        mlflow_model,
        loader_module="custom_sktime_flavor",  # Your module name
        model_path=model_data_subpath,
        conda_env=_CONDA_ENV_FILE_NAME,
        python_env=_PYTHON_ENV_FILE_NAME,
    )

    # Add custom flavor configuration
    mlflow_model.add_flavor(
        FLAVOR_NAME,
        pickled_model=model_data_subpath,
        sktime_version=sktime.__version__,
        serialization_format=serialization_format,
    )

    # Save MLmodel configuration file
    mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))


def log_model(sktime_model, artifact_path, **kwargs):
    """Log sktime model to current MLflow run"""
    return Model.log(
        artifact_path=artifact_path,
        flavor=custom_sktime_flavor,  # Your module reference
        sktime_model=sktime_model,
        **kwargs,
    )


def load_model(model_uri):
    """Load sktime model from MLflow format"""
    local_model_path = mlflow.artifacts.download_artifacts(model_uri)

    # Read flavor configuration
    model_config = Model.load(os.path.join(local_model_path, MLMODEL_FILE_NAME))
    flavor_conf = model_config.flavors[FLAVOR_NAME]

    # Load the pickled model
    model_path = os.path.join(local_model_path, flavor_conf["pickled_model"])
    with open(model_path, "rb") as f:
        return pickle.load(f)


class SktimeModelWrapper:
    """PyFunc wrapper for sktime models"""

    def __init__(self, sktime_model):
        self.sktime_model = sktime_model

    def predict(self, context, model_input):
        """Predict using configuration DataFrame"""
        if len(model_input) != 1:
            raise ValueError("Configuration DataFrame must have exactly 1 row")

        config = model_input.iloc[0].to_dict()
        predict_method = config.get("predict_method", "predict")

        if predict_method == "predict":
            fh = config.get("fh", None)
            X = config.get("X", None)
            return self.sktime_model.predict(fh=fh, X=X)

        elif predict_method == "predict_interval":
            fh = config.get("fh", None)
            X = config.get("X", None)
            coverage = config.get("coverage", 0.9)
            return self.sktime_model.predict_interval(fh=fh, X=X, coverage=coverage)

        else:
            raise ValueError(f"Unsupported predict_method: {predict_method}")


def _load_pyfunc(path):
    """Load model as PyFunc"""
    model = load_model(path)
    return SktimeModelWrapper(model)

Usage Example

import mlflow
import pandas as pd
from sktime.forecasting.naive import NaiveForecaster
from sktime.datasets import load_longley

# Train model
y, X = load_longley()
forecaster = NaiveForecaster()
forecaster.fit(y, X=X)

# Log with custom flavor
with mlflow.start_run():
    custom_sktime_flavor.log_model(
        sktime_model=forecaster, artifact_path="custom_forecaster"
    )
    model_uri = mlflow.get_artifact_uri("custom_forecaster")

# Load and use natively
loaded_model = custom_sktime_flavor.load_model(model_uri)
native_predictions = loaded_model.predict(fh=[1, 2, 3])

# Load as PyFunc for serving
loaded_pyfunc = mlflow.pyfunc.load_model(model_uri)

# Create configuration for PyFunc prediction
config_df = pd.DataFrame(
    [
        {
            "predict_method": "predict_interval",
            "fh": [1, 2, 3, 4],
            "coverage": [0.9, 0.95],
            "X": X.tail(4).values.tolist(),  # JSON serializable
        }
    ]
)

pyfunc_predictions = loaded_pyfunc.predict(config_df)

Model Serving

# Serve your custom flavor model
mlflow models serve -m runs:/RUN_ID/custom_forecaster --host 127.0.0.1 --port 5000

# Request predictions from served model
import requests
import pandas as pd

config_df = pd.DataFrame([{"predict_method": "predict", "fh": [1, 2, 3, 4]}])

response = requests.post(
    "http://127.0.0.1:5000/invocations",
    json={"dataframe_split": config_df.to_dict(orient="split")},
)

predictions = response.json()

Framework Support Matrix

By Use Case

Use Case	Frameworks	Installation	Key Features
Time Series Forecasting	Sktime, Orbit, StatsForecast	pip install sktime[mlflow]	Unified API, multiple algorithms
Anomaly Detection	PyOD	pip install mlflavors	40+ detection algorithms
Visualization	Altair, Plotly via VizMod	pip install mlflow-vizmod	Interactive plots as models
Synthetic Data	SDV	pip install mlflavors	Privacy-preserving data generation
Big Data ML	BigML	pip install bigmlflow	Cloud-based supervised learning

Integration Patterns

Standard Pattern

Most community flavors follow this pattern:

import mlflow
import community_flavor

# 1. Train your model
model = SomeFrameworkModel()
model.fit(training_data)

# 2. Log with MLflow
with mlflow.start_run():
    community_flavor.log_model(
        model=model,
        artifact_path="model",
        # Framework-specific parameters
        serialization_format="pickle",
        custom_config={"param": "value"},
    )

# 3. Load for inference
loaded_model = community_flavor.load_model(model_uri)
predictions = loaded_model.predict(new_data)

# 4. Load as PyFunc for generic serving
pyfunc_model = community_flavor.pyfunc.load_model(model_uri)
generic_predictions = pyfunc_model.predict(input_dataframe)

Serving Pattern

Configuration-based serving for complex models:

import pandas as pd

# Many community flavors use configuration DataFrames
# for complex inference parameters
config_df = pd.DataFrame(
    [
        {
            "predict_method": "predict_interval",  # What type of prediction
            "fh": [1, 2, 3, 4],  # Forecast horizon
            "coverage": [0.9, 0.95],  # Confidence intervals
            "X": exogenous_data.tolist(),  # Additional features
            "custom_param": "value",  # Framework-specific options
        }
    ]
)

# Use configuration with PyFunc model
pyfunc_model = community_flavor.pyfunc.load_model(model_uri)
predictions = pyfunc_model.predict(config_df)

Deployment Pattern

Production deployment workflow:

# 1. Register model in MLflow Model Registry
mlflow.register_model(
    model_uri="runs:/RUN_ID/model",
    name="production_forecaster"
)

# 2. Transition to production stage
client = mlflow.MlflowClient()
client.transition_model_version_stage(
    name="production_forecaster",
    version=1,
    stage="Production"
)

# 3. Serve model
# Option A: Local serving
mlflow models serve \
  -m "models:/production_forecaster/Production" \
  --host 0.0.0.0 --port 5000

# Option B: Cloud deployment (Azure ML example)
mlflow deployments create \
  -t azureml \
  -m "models:/production_forecaster/Production" \
  --name forecaster-service

Best Practices

Development Guidelines

1. Follow MLflow Conventions - Implement save_model(), log_model(), load_model() functions. Add PyFunc flavor for generic inference. Include comprehensive error handling.

2. Configuration Management - Use single-row DataFrames for complex inference parameters. Make all parameters JSON-serializable for REST API serving. Provide sensible defaults for optional parameters.

3. Testing Strategy - Test save/load roundtrip functionality. Verify PyFunc compatibility. Test model serving with sample requests.

Performance Optimization

# Efficient serialization for large models
def save_model(model, path, serialization_format="pickle"):
    if serialization_format == "joblib":
        # Use joblib for sklearn-compatible models
        import joblib

        joblib.dump(model, os.path.join(path, "model.joblib"))
    elif serialization_format == "cloudpickle":
        # Use cloudpickle for complex models with custom objects
        import cloudpickle

        with open(os.path.join(path, "model.pkl"), "wb") as f:
            cloudpickle.dump(model, f)

Error Handling

def load_model(model_uri):
    try:
        # Attempt to load model
        return _load_model_internal(model_uri)
    except Exception as e:
        raise mlflow.exceptions.MlflowException(
            f"Failed to load {FLAVOR_NAME} model. "
            f"Ensure model was saved with compatible version. Error: {str(e)}"
        )

Community Resources

Contributing New Flavors

1. Create GitHub Repository - Follow naming convention: mlflow-{framework}. Include comprehensive documentation. Add example notebooks.

2. Package Structure

   mlflow-myframework/
   ├── setup.py
   ├── README.md
   ├── mlflow_myframework/
   │   ├── __init__.py
   │   └── flavor.py
   ├── examples/
   │   └── example_usage.ipynb
   └── tests/
       └── test_flavor.py

3. Documentation Requirements - Installation instructions. Basic usage examples. API reference. Model serving examples.

Getting Help

MLflow Discussions: GitHub Discussions. Community Slack: Join the MLflow community workspace. Stack Overflow: Tag questions with mlflow and framework name. Framework-Specific: Check individual flavor repositories for issues.

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Ready to extend MLflow? Start by exploring the existing community flavors, then consider contributing your own flavor for frameworks not yet supported!