This talk was given at H2O World 2018 NYC and can be viewed here: https://youtu.be/oxLZZMR1lVY Description: Driverless AI is H2O.ai's latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy, especially for time-series problems. With Driverless AI, data scientists of all proficiency levels can train and deploy modeling pipelines with just a few clicks from the GUI. Advanced users can use the client API from Python. Driverless AI builds hundreds or thousands of models under the hood to select the best feature engineering and modeling pipeline for every specific problem such as churn prediction, fraud detection, real-estate pricing, store sales prediction, marketing ad campaigns and many more. To speed up training, Driverless AI uses highly optimized C++/CUDA algorithms to take full advantage of the latest compute hardware. For example, Driverless AI runs orders of magnitudes faster on the latest Nvidia GPU supercomputers on Intel and IBM platforms, both in the cloud or on premise. Driverless AI is fully supported on all major cloud providers. There are two more product innovations in Driverless AI: statistically rigorous automatic data visualization and machine learning interpretability with reason codes and explanations in plain English. Both help data scientists and analysts to quickly validate the data and the models. In this talk, we explain how Driverless AI works and show how easy it is to reach top 5% rankings for several highly competitive Kaggle competitions. (edited) Speaker's Bio: Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.

1. Arno Candel, PhD
Chief Technology Officer
H2O.ai
@arnocandel
Driverless AI

3. Shortage of Data Scientists
FOr THOSE ABOUT TO KAGGLE, WE SALUTE YOU!

4. Looks great, BUT: It’s wrong.
Spring of AI: Machine Learning is Everywhere

5. Mistake [v1] Correction [v2]
Automation needed to
avoid human error

7. Driverless AI: Automates Data Science and ML Workflows
Driverless AI

8. 2 months for Grandmasters — 2 hours for Driverless AI
single run, fully automated: 2h on DGX Station! 6h on PC
Driverless AI: 13th place in private LB at Kaggle (out of 2926)
Driverless AI: top 0.5% in BNP Paribas Kaggle competition

9. Secret Sauce: 1) Grandmaster Feature Engineering
Numerical/Categorical Interactions, Target
Encoding, Clustering, Dimensionality Reduction,
Weight of Evidence, etc.
Time-Series: Lags and historical aggregates
with causality constraints

10. Secret Sauce: 2) Grandmaster Pipeline Tuning + Validation
19,000 features tested
1,000 models trained
reliable generalization estimates (overfitting avoidance)
Example: Driverless AI BNP Paribas on 3-GPU workstation
evolutionary strategies
DOI: 10.1126/science.aaa9375
MTV
1 final optimal
scoring pipeline
massively parallel processing
(multi-CPU, multi-GPU)

11. • Automatic handling of time groups (e.g. [time, store_id, department_id])
• Robust validation framework
• Accounting for time gaps between train & test
• Accounting for length of forecast horizon the user is interested in
• Comprehensive set of recipes for time series specific feature engineering
• Date features like day of week, day of month etc.
• Optimal (target)-lags taking account of detected time groups
• Interactions of lagged-features
• Exponentially Weighted Moving Averages of n-th order differentiated past
information
• Aggregation of past information (mean, std, sums, etc.) across time groups and
for different time intervals (e.g. every week, every 2 weeks etc.)
• Fully integrated into Driverless AI‘s optimization pipeline
Time Series in Driverless AI

12. time:
Gap=1 | Forecast Horizon=2
invalid lag size (no information available)
valid lag size (information available)
Time Series in Driverless AI
• Automatic Selection or Manual Control for:
• Forecast Horizon
• Gap between Training and Production

13. https://web.stanford.edu/~hastie/Papers/ESLII.pdf
http://www.deeplearningbook.org
Statistical Learning vs Deep Learning - We Do Both!
Typically better for structured data
(CSV, SQL, Transactional)
Typically better for unstructured data
(Images, Video, Audio, Text)
GLM/CART/RF/GBM/XGBoost
K-Means/PCA/SVD
TensorFlow Deep Learning

14. Feature v1.0 v1.1
v1.2
(NOW)
v1.3
Kaggle Grandmaster Recipes for i.i.d. data
Automatic Visualization
Machine Learning Interpretability
GBM (XGBoost) for high accuracy incl. stacked ensembles
5-minute Install with Docker for Linux/Mac/Windows - Cloud/OnPrem
Standalone Python Scoring Pipeline
Hardware acceleration: NVIDIA GPUs (DGX-1 etc.)
User Management and Security (LDAP/Kerberos)
Data Connectors: NFS/HDFS/S3/GCS/BigQuery, CSV/Excel/Parquet/Feather
GLM (Linear models) for high interpretability
Native Installer: RPM/DEB
Cloud Neutral: Amazon/Microsoft/Google
Kaggle Grandmaster Recipes for Time-Series
IBM Power8/Power9
Deep Learning TensorFlow Models
Standalone Java Scoring Pipeline (MOJO)
Deep Learning for NLP (Text)
Model Management
Driverless AI Roadmap