Driverless AI is an automated machine learning platform created by H2O.ai that can complete an entire machine learning workflow from data to deployment in as little as 2 hours. It uses techniques developed by H2O Grandmasters such as automated feature engineering, model tuning, and ensemble building to generate high performing models with little to no input from users. Driverless AI supports both structured and unstructured data types including text/NLP and time series data and generates documentation of all modeling steps.

1. Introduction to
H2O Driverless AI
Arno Candel
CTO
H2O.ai
@arnocandel

2. • Founded in 2011
• 110+ employees
• Mountain View, CA
• VC funded
• Open-Source Culture
H2O.ai is a Leader in the Gartner Magic Quadrant

3. Shortage of Data Scientists

5. Data
Processing
Feature
Engineering
Model
Tuning
Final Ensemble
Training
Scoring
Pipeline
H2O Driverless AI - From Data to Deployment

7. 2 months for Grandmasters — 2 hours for Driverless AI
single run, fully automated: 2h on DGX Station! 6h on PC
Driverless AI: 10th place in private LB at Kaggle (out of 2926)
Driverless AI: top 10 in BNP Paribas Kaggle competition

8. Automatic Visualization
Scalable outlier detection
(no sampling)
Contains novel statistical algorithms to
only show “relevant” aspects of the data
(coming soon: automated data cleaning)

9. MLI - Machine Learning Interpretation
Gain confidence in models before deploying them!

10. http://h2o.ai
21-day free trial
Easy installation:
Native and Dockerized deployment options

11. 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

12. 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)

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. time:
Gap=1 | Forecast Horizon=2
invalid lag size (no information available)
valid lag size (information available)
1 2 3 4 5 6 7 8 9 10 11 12
[Gap]
"[ Gap ]" "8" "9" [Gap] [Gap]
test
tvs train tvs valid
train
test
Time Series in Driverless AI
• Automatic Selection or Manual Control for:
• Forecast Horizon
• Gap between Training and Production

15. • 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

16. Text / Natural Language Processing in Driverless AI
https://blog.h2o.ai/2018/09/automatic-feature-engineering-text-analytics-latest-addition-kaggle-grandmasters-recipes/
i.i.d and Time-Series Recipes
NLP Recipes: Statistical and Deep Learning
on roadmap
more information:
Recipes can mix & match

17. Text / Natural Language Processing in Driverless AI

18. Every step is documented automatically
(custom templates are supported)
AutoDoc - Automatic Documentation of Experiments
Data
Processing
Feature
Engineering
Model
Tuning
Final Ensemble
Training
Scoring
Pipeline

25. Feature v1.0 v1.1 v1.2 v1.3
v1.4
(NOW)
v1.5 v2.0
Kaggle Grandmaster Recipes for i.i.d. data
Automatic Visualization
Machine Learning Interpretability
GBM (XGBoost) for high accuracy incl. stacked ensembles (CPU/GPU)
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 (CPU/GPU)
Native Installer: RPM/DEB
Cloud Neutral: Amazon/Microsoft/Google
Kaggle Grandmaster Recipes for Time-Series
IBM Power8/Power9
AutoDoc
Deep Learning TensorFlow Models (CPU/GPU)
Standalone Java Scoring Pipeline (MOJO)
Deep Learning for NLP / Text (CPU/GPU)
LightGBM Models (CPU/GPU)
Improved Time-Series Recipes (Multiple Windows), MLI for Time-Series
Improved Final Ensemble
Local Feature Brain
C++ Scoring Pipeline
FTRL Models
Multi-Node Training and much more (based on customer demand)
Driverless AI Roadmap

26. Live Demo
16th (of 2926)