H2O World - Welcome to H2O World with Arno Candel

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H2O World 2015 - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

Software

We l c o m e ' t o ' H 2 O ' Wo r l d
Sri'&'H2O'Team'

Data
Science
is
a
Team
Sport!

Culture
Matters!

Open
Source
Breeds
Courage!

Community
Matters!

Every
generation
needs
to
make
its
own
history!

Code
is
conversation
with
Customer!
Great
Product
Matters!

Accuracy
with
Speed
and
Scale
HDFS%
S3%
SQL%%
NoSQL%
CLASSIFICATION%
REGRESSION%
FEATURE%
ENGINEERING%
IN4MEMORY%
MAP%REDUCE/FORK%JOIN%
COLUMNAR%COMPRESSION%
DEEP%LEARNING%
PCA,%GLM,%COX%
RANDOM%FOREST%/%GBM%
ENSEMBLES%
FAST % M O D E L I NG % E NG I NE %
Streaming%
NANO % FAST % JAVA% S CO R I NG% E NGI NES %
MATRIX%
FACTORIZATION% CLUSTERING%
MUNGING%

What’s
New
in
H2O-‐3
H2O-‐3
vs
H2O-‐2:

• Total
rewrite
of
the
core
in
Java:
built
for
data
scientists
AND
developers!

• Unique
Flow
GUI
(Notebook
and
more)

• REST
Schemas
for
self-‐describing
API
for
all
methods/algos

• New
R
client:
cleaner,
faster

• Sparkling
Water:
H2O
is
the
Killer
App
on
Spark

• Fully
featured
Python
client
(incl.
Pipelines,
scikit-‐learn
look&feel)

• New
expression
parser
&
backend
execution
engine
for
R,
Py,
Flow

• New
Algo:
GLRM
-‐
Generalized
Low
Rank
Modeling 
(unifies
PCA,
K-‐Means,
Matrix
Factorization,
Imputation,
etc.)

• New
Solvers
for
GLM:
Coordinate
Descent
and
L-‐BFGS
continued…

What’s
New
in
H2O-‐3
Additional
New
Features:

• Grid
Search
for
all
Algorithms
(R/Py/Flow)

• N-‐fold
Cross-‐Validation
for
all
Algorithms

• Early
Stopping
(check
for
convergence)
for
GBM/DRF/DL

• Stochastic
GBM
(row/col
sampling)

• Distributions
(Gaussian,
Laplace,
Poisson,
Gamma,
Tweedie)
for
GBM/DL

• Improved
sparse
data
handling
for
DL

• Multi-‐node
auto-‐tuning
for
DL

• Multinomial
GLM

• Scalable
Scatter
Plots
for
numeric
and
categorical
data

• Big-‐Big
Joins
(“distributed
data.table”)
-‐
in
QA
…and
many
more!

Convergence-‐Based
Early
Stopping
in
H2O
Before:
trains
too
long,
but
at
least

overwrite_with_best_model=true

prevents
overfitting
(returns
the

model
with
lowest
validation
error)
Now:
specify
additional
convergence
criterion:
E.g.

stopping_rounds=5,
stopping_metric=“MSE”,

stopping_tolerance=1e-‐3,
to
stop
as
soon
as
the
moving

average
(length
5)
of
the
validation
MSE
does
not

improve
by
at
least
0.1%
for
5
consecutive
scoring
events
validation
error
training
error
overwrite_with_best_model=true
training
time
/
epochs
training
time
/
epochsUse
Flow
to
inspect
the
model
Early
stopping
saves
tons
of
time
Best
Model
Deep
Learning
with
Higgs
data

What
do
these
stickers
mean?
I have H2O
Installed
I have Python
installed
I have R
installed
I have the H2O
World data
sets
Pick
up
stickers
or
get
install
help
at
the

information
booth

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Patrick Hall, Professor, AI Risk Management, The George Washington University H2O Open Source GenAI World SF 2023 Language models are incredible engineering breakthroughs but require auditing and risk management before productization. These systems raise concerns about toxicity, transparency and reproducibility, intellectual property licensing and ownership, disinformation and misinformation, supply chains, and more. How can your organization leverage these new tools without taking on undue or unknown risks? While language models and associated risk management are in their infancy, a small number of best practices in governance and risk are starting to emerge. If you have a language model use case in mind, want to understand your risks, and do something about them, this presentation is for you!

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Dr. Alexy Khrabrov, Open Source Science Community Director, IBM H2O Open Source GenAI World SF 2023 In this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.

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Megan Kurka, Vice President, Customer Data Scientist, H2O.ai H2O Open Source GenAI World SF 2023 Discover the transformative power of Applied Gen AI. Learn how the H2O team builds customized applications and workflows that integrate capabilities of Gen AI and AutoML specifically designed to address and enhance financial use cases. Explore real world examples, learn best practices, and witness firsthand how our innovative solutions are reshaping the landscape of finance technology.

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Pascal Pfeiffer, Principal Data Scientist, H2O.ai H2O Open Source GenAI World SF 2023 This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.

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Numerai is an open, crowd-sourced hedge fund powered by predictions from data scientists around the world. In return, participants are rewarded with weekly payouts in crypto. In this talk, Joe will give an overview of the Numerai tournament based on his own experience. He will then explain how he automates the time-consuming tasks such as testing different modelling strategies, scoring new datasets, submitting predictions to Numerai as well as monitoring model performance with H2O Driverless AI and R.

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In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization. To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g Speaker: Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)

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The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey. To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course To find the Youtube video about this presentation: https://youtu.be/PJgr2epM6qs Speakers: Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist) Ingrid Burton (H2O.ai - CMO)

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6. What’s New in H2O-‐3 H2O-‐3 vs H2O-‐2: • Total rewrite of the core in Java: built for data scientists AND developers! • Unique Flow GUI (Notebook and more) • REST Schemas for self-‐describing API for all methods/algos • New R client: cleaner, faster • Sparkling Water: H2O is the Killer App on Spark • Fully featured Python client (incl. Pipelines, scikit-‐learn look&feel) • New expression parser & backend execution engine for R, Py, Flow • New Algo: GLRM -‐ Generalized Low Rank Modeling  (unifies PCA, K-‐Means, Matrix Factorization, Imputation, etc.) • New Solvers for GLM: Coordinate Descent and L-‐BFGS continued…

7. What’s New in H2O-‐3 Additional New Features: • Grid Search for all Algorithms (R/Py/Flow) • N-‐fold Cross-‐Validation for all Algorithms • Early Stopping (check for convergence) for GBM/DRF/DL • Stochastic GBM (row/col sampling) • Distributions (Gaussian, Laplace, Poisson, Gamma, Tweedie) for GBM/DL • Improved sparse data handling for DL • Multi-‐node auto-‐tuning for DL • Multinomial GLM • Scalable Scatter Plots for numeric and categorical data • Big-‐Big Joins (“distributed data.table”) -‐ in QA …and many more!

8. Convergence-‐Based Early Stopping in H2O Before: trains too long, but at least overwrite_with_best_model=true prevents overfitting (returns the model with lowest validation error) Now: specify additional convergence criterion: E.g. stopping_rounds=5, stopping_metric=“MSE”, stopping_tolerance=1e-‐3, to stop as soon as the moving average (length 5) of the validation MSE does not improve by at least 0.1% for 5 consecutive scoring events validation error training error overwrite_with_best_model=true training time / epochs training time / epochsUse Flow to inspect the model Early stopping saves tons of time Best Model Deep Learning with Higgs data

9. What do these stickers mean? I have H2O Installed I have Python installed I have R installed I have the H2O World data sets Pick up stickers or get install help at the information booth

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