1. Driverless AI can be used across many industries like banking, healthcare, telecom, and marketing to save time and money through tasks like fraud detection, customer churn prediction, and personalized recommendations. 2. The document highlights new features in Driverless AI 1.7.1 including improved time series recipes, natural language processing features, automatic visualization, and machine learning interpretability tools. 3. Driverless AI provides fully automated machine learning through techniques such as automatic feature engineering, model tuning, standalone scoring pipelines, and massively parallel processing to find optimal solutions.

1. Aug 20 2019
Arno Candel, CTO H2O.ai
@ArnoCandel
What’s New in H2O Driverless AI

2. Hands-On Training
1. go to aquarium.h2o.ai
2. create account
3. log In

3. 4. select Arno Candel’s Driverless AI 1.7.1
5. start Lab
6. wait… Then click URL when instructed
Hands-On Training

5. Confidential3
Industry Use Cases
Save Time. Save Money. Gain a Competitive Advantage.
Wholesale / Commercial
Banking
• Know Your Customers (KYC)
• Anti-Money Laundering (AML)
Card / Payments Business
• Transaction frauds
• Collusion fraud
• Real-time targeting
• Credit risk scoring
• In-context promotion
Retail Banking
• Deposit fraud
• Customer churn prediction
• Auto-loan
Financial Services
• Early cancer detection
• Product recommendations
• Personalized prescription
matching
• Medical claim fraud detection
• Flu season prediction
• Drug discovery
• ER and hospital
management
• Remote patient monitoring
• Medical test predictions
Healthcare
• Predictive maintenance
• Avoidable truck-rolls
• Customer churn prediction
• Improved customer viewing
experience
• Master data management
• In-context promotions
• Intelligent ad placements
• Personalized program
recommendations
Telecom
• Funnel predictions
• Personalized ads
• Credit scoring
• Fraud detection
• Next best offer
• Next best customer
• Smart profiling
• Prediction
• Customer recommendations
• Ad predictions and spend
Marketing and Retail
Driverless AI: Used Across Many Industries

6. Confidential12
Using “AI to do AI” with H2O Driverless AI
21 day free trial for Driverless AI
Gives every data scientist the
tools to be successful
Automatic feature engineering,
modeling, visualization, and
interpretability
Automatic standalone scoring
pipelines – convert data to code
Provides easy to understand
results and visualizations
….. in a single platform
Confidential12

8. 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

9. https://www.h2o.ai/blog/
Driverless AI — Teamwork and Maker’s Culture

10. Automatic Visualization
Scalable outlier detection
(no sampling)
Contains novel statistical algorithms to
only show “relevant” aspects of the data
(soon: actionable recipes and interactive visualization)

11. MLI - Machine Learning Interpretation
Gain confidence in models before deploying them!
Shapley values, partial dependence, ICE, original and transformed features

12. Confidential!12
MLI for i.i.d. and Time Series Data
Global approximate model behavior/interactions
Global feature importance
Shapley
DT
Global feature behavior
Reason codes
K-LIME
PDP
Model
Local feature importance
Local feature behavior
ICE
Local approximate model behavior
Model
RF
• Added residual plot for regression problems (keeping all outliers intact)
• Added confusion matrix as default metric display for multinomial problems
• Added Partial Dependence (PD) and Individual Conditional Expectation (ICE)
plots for Driverless AI models in MLI GUI
• Added ability to search by ID column in MLI GUI
• Added ability to run MLI PD/ICE on all features
• Added ability to handle multiple observations for a single time column in
MLI TS by taking the mean of the target and prediction where applicable
• Added ability to handle integer time column in MLI TS
• MLI TS will use train holdout predictions if there is no test set provided

13. 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

14. 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)

15. https://web.stanford.edu/~hastie/Papers/ESLII.pdf
http://www.deeplearningbook.org
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
Secret Sauce: 3) Statistical Learning & Deep Learning

16. Feature v1.0 v1.1 v1.2 v1.3 v1.4
v1.5
v1.6 LTS
v1.7
v1.8 LTS
v2.0
Kaggle Grandmaster Recipes for i.i.d. data, XGBoost Models
Automatic Visualization
Machine Learning Interpretability
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
Native Installer (RPM/DEB) and Cloud Neutral: Amazon/Microsoft/Google
Kaggle Grandmaster Recipes for Time-Series
Automatic Documentation
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
Local Feature Brain
Improved Scalability, FTRL Models, Model Diagnostics, Data Splitting, Retrain Final Model, etc.
C++ Scoring Pipeline (Runtime for MOJO), with Python and R bindings, TensorFlow MOJO
Improved Time-Series Recipes (backtesting, test-time augmentation, single time-series)
Project Workspace
Bring Your Own Recipe (Transformers, Models, Scorers) - Custom Python Code
Model Monitoring
R client API
Multi-Node and Multi-User Deployment
Driverless AI Roadmap v1.7.1 AUG ‘19
Lots of New Features in 1.7.x!

17. 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
• Backtesting Scheme
Automatic Lag Feature Generation

18. Driverless AI - NLP Features - Count
Image Source : STHDA

19. Driverless AI - NLP Features - TFIDF
Image Source : Towards Data Science

20. Driverless AI - NLP Features - Word Embeddings
Image Source : Tensorflow

21. Driverless AI - NLP Recipe
Text
Feature
TFIDF /
Count
Truncated
SVD
Txt:<feat>
TFIDF
Word
Embedding
Word
Embedding
Character
Embedding
Linear /
Logistic Model
CNN Model
RNN Model
CNN Model
TxtTE:<feat>
TextCNN_TE:
<feat>
TextBiGRU_TE:
<feat>
TextCharCNN:
<feat>

22. Data Connectors
Driverless AI connects to data sources
anywhere on premise or in the Cloud.
Current approach: Scale-up
Move the data once onto dedicated
compute hardware
Coming soon: Scale-out (and -up)
Distribute across multiple nodes

23. Overrides for Column Types and Date/Time Formats
Set data science column
types, e.g.:
integer field can be categorical
only, and not treated as numeric
(0=hi, 1=lo, 2=n/a)
string field can be text and
categorical, for NLP features and
grouping
Custom datetime format
string like “%d.%m.%Y”

24. Project Workspace - Organize & Compare

25. Project Workspace - Organize & Compare

26. Expert Settings for Experiments

27. R and Python Clients — Remote Control Driverless AI
Clients for Python and R
• Automate workflows with scripts
• Connect to powerful server remotely
• Full functionality of Driverless AI is
available from Python and R
• Secure connection (LDAP, Kerberos)

28. MOJO2 Scoring Runtime for Java, Python and R
MOJO2 Runtime
• Stand-alone scoring based on Protobuf
serialization
• No approximations, entire ML pipeline
• Low-Latency scoring
• No other dependencies
• Can be embedded in applications
• Python, R, Java (Scala), C++ coming

29. Visualization of MOJO2 Pipeline

30. One-Click Deployment: Amazon AWS Lambda

31. One-Click Deployment: Local REST Server

32. BYOR — Bring Your Own Recipes!
https://www.h2o.ai/products-h2o-driverless-ai-recipes/

33. Open-Source Recipes - Makers Gonna Make!
https://github.com/h2oai/driverlessai-recipes

34. Open-Source Recipes - Makers Gonna Make!

35. BYOR — Bring Your Own Recipes!

36. Easy Uplift by Adding NLP Sentiment Recipe
LogLoss = 0.48LogLoss = 0.60 LogLoss = 0.49
Built-In TensorFlow NLP RecipesBuilt-In Tf/IdF NLP Recipes With custom Sentiment recipe

37. Combined multiple models together for better Accuracy
MAE = 1744 (DAI+Prophet+ARIMA) MAE = 2060 (FaceBook Prophet) MAE = 3442 (ARIMA)

38. MLI for Custom Time-Series Recipes
Default Recipe (Lags) Prophet Recipe

39. More details:
FAQ / Architecture Diagram etc.
https://github.com/h2oai/driverlessai-recipes

40. AutoDoc - Automatic Documentation of Experiments
Full transparency into automation process:
Validation scheme, model tuning, feature selection, ensembling, metrics, diagnostics.
Includes custom recipes, fully editable/customizable Word document.

41. AutoDoc - Automatic Documentation of Experiments
Now also with partial dependence plots!

42. Take a Test Drive!
http://h2o.ai

43. Chat with Us!
https://www.h2o.ai/community/

44. Webinars

45. Driverless AI Docs & Tutorials: docs.h2o.ai
https://h2oai.github.io/tutorials/

46. Live Demo