AutoML on Databricks.
Automating Machine Learning pipelines at scale
Try our notebook > Contact us > Get Started Documentation Resources FAQ AutoML on Databricks automates Machine Learning pipelines from feature engineering, model search, hyperparameter tuning, and inference while providing data scientist s with the flexibility and control they need.
How it works.
Databricks automates various steps of the data science workflow including augmented data preparation, visualization, feature engineering, hyperparameter tuning, model search, and finally automatic model tracking, reproducibility, and deployment, through a combination of native product offerings, partnerships, .
And custom solutions for a fully controlled and transparent AutoML experience
See for example how you can run hyperparameter tuning at scale on Databricks with enhanced Hyperopt and MLflow integration : Benefits.
Scalability: Automatically scale up and down your workloads and speed up training time with out-of-the-box optimizations for the most popular ML frameworks.
Control: Choose algorithms better suited for the task in either single node or multi-node environment , and limit the number of runs to keep costs down.
Ease of use: Automatically log results with MLflow tracking and parallelize hyperparameter search with Hyperopt on Databricks.
Unification: Run all AutoML steps on the same platform
from ETL to model training and inference, securely, collaboratively, and at scale.
Out of the Box.
MLflow Experiments Tracking Track
compare, and visualize hundreds of thousands of experiments using open source or Managed MLflow.
Automated Hyperparameter Tuning
for Distributed Machine Learning Deep integration with PySpark MLlib’s Cross Validation to automatically track MLlib experiments in MLflow.
Automated Hyperparameter Tuning for Single-node Machine Learning Optimized and distributed hyperparameter search with enhanced Hyperopt and automated tracking to MLflow.
Automated Model Search for Single-node Machine Learning Optimized and distributed conditional hyperparameter search with enhanced Hyperopt and automated tracking to MLflow.
Databricks Labs AutoML Toolkit Automated end-to-end model building pipeline is available via Databricks Labs custom solutions .
Contact us for more information
Microsoft Azure Machine Learning Azure Databricks integrates with Microsoft Azure Machine Learning and enables access to the service’s automated machine learning capabilities, and together these provide an end-to-end solution for machine learning on Azure.
DataRobot DataRobot integration on Databricks brings the power of auto-modeling to Databricks users, allowing them to quickly determine and use the best machine learning model for their problem.
Detecting Bias with SHAP Blog Post.
Hyperparameter Tuning with MLflow, Apache Spark MLlib and Hyperopt Blog Post.
Enhanced Hyperparameter Tuning & Optimized AWS Storage with Databricks Runtime 5.4 ML Blog Post.
Using AutoML Toolkit to Automate Loan Default Predictions Webinars.
Automated Hyperparameter Tuning, Scaling and Tracking on Databricks Webinars.
How to Automate Machine Learning and Scale Delivery Tutorials.
Hyperparameter Tuning Documentation Tutorials.
MLflow integrations with H20.ai GPyOpt, HyperOpt Notebooks.
MLlib + Automated MLflow Tracking Notebooks.
Distributed Hyperopt + Automated MLflow Tracking Notebooks.
Basic Introduction to DataRobot via API Videos.
Automating Predictive Modeling at Zynga with PySpark and Pandas UDFs Videos.
Best Practices for Hyperparameter Tuning with MLflow Videos.
Advanced Hyperparameter Optimization for Deep Learning with MLflow Docs.
Hyperparameter Tuning Documentation Docs.
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