---
title: Advanced ML and API approaches
description: Advanced usage of the DataRobot API that you can add to your experiment workflow.

---

# Advanced ML and API approaches

Topic | Describes... |
----- | ------ |
[Track ML experiments with MLFlow](mlflow) | Learn how to programmatically build a model with DataRobot, and then export and host the model in AWS SageMaker.
[Customize lift charts](custom-lift-chart) | Leverage popular Python packages with DataRobot's Python client to recreate and augment DataRobot's lift chart visualization.
[Select models using custom metrics](ai-custom-metrics) | This AI Accelerator demonstrates how one can leverage DataRobot's Python client to extract predictions, compute custom metrics, and sort their DataRobot models accordingly.
[Tune blueprints for preprocessing and model hyperparameters](opt-grid) | Learn how to access, understand, and tune blueprints for both preprocessing and model hyperparameters.
[Fine-tune models with Eureqa](tune-eureqa) | Apply symbolic regression to your dataset in the form of the Eureqa algorithm.
[Migrate a model to a new cluster](model-migrate) | Download a deployed model from DataRobot cluster X, upload it to DataRobot cluster Y, and then deploy and make requests from it.
[Feature Reduction with FIRE](fire) | Learn about the benefits of Feature Importance Rank Ensembling (FIRE)&mdash;a method of advanced feature selection that uses a median rank aggregation of feature impacts across several models created during a run of Autopilot. |
[Creating Custom Blueprints with Composable ML](custom-bp-nb) | Customize models on the Leaderboard using the Blueprint Workshop. |
[Prepare and leverage image data with Databricks](image-databricks) | Import image files using Spark and prepare them into a data frame suitable for ingest into DataRobot. |
[Gather churn prediction insights with the Streamlit app](streamlit-app) | Use the Streamlit churn predictor app to present the drivers and predictions of your DataRobot model. |
[Perform multi-model analysis](ml-analysis) | Use Python functions to aggregate DataRobot model insights into visualizations. |
[Enrich data using the Hyperscaler API](enrich-gcp) | Call the GCP API and enrich a modeling dataset that predicts customer churn. |
[Predict lumber prices with Ready Signal and time series forecasts](ready-signal) | Use Ready Signal to add external control data, such as census and weather data, to improve time series predictions. |
[Build a model factory with Python multithreading](python-multi) | How to use the Python threading library to build a model factory. |
[Predict flight delays starter use case](flight-delays) | Designed for DataRobot trial users, experience an end-to-end DataRobot workflow using a use case that predicts flight delays. |
