A few Challenges to Make Machine Learning Easy

June 3rd, 2013BigML Inc, 2013
Challenges to
Make Machine Learning Easy
ACM San Francisco Bay Area Professional Chapter
Francisco J Martin, Ph.D.
BigML Co-founder & CEO
eBay Whitman Campus

June 3rd, 2013BigML Inc, 2013 2
Expert: Published papers at KDD, ICML, NIPS, etc or
developed own ML algorithms used at large scale.
Sampling the Audience
Aficionado: Understands pros/cons of different
techniques and/or can tweak algorithms as needed.
Newbie: Just taking Coursera ML class or reading an
introductory book to ML.
Absolute beginner: ML sounds like science fiction
Practitioner: Very familiar with ML packages (Weka,
Scikit, R, etc).

Data, data
everywhere
A special report on managing information
Why make ML easy?
In the age of data, Machine
Learning is the key component to:
‣ make data-driven decisions
‣ develop smart applications
‣ build predictive analytics

June 3rd, 2013BigML Inc, 2013
However, Machine Learning is
COMPLEX:
‣tools are complicated and do
not scale well
‣solutions are costly
‣e x p e r t s w i t h i n d u s t r y
experience are scarce
4
Why make ML easy?
http://ttic.uchicago.edu/~samory/

Why make ML easy?

April, 2013BigML Inc, 2013 7
BigML
A cloud-based service that makes
Machine Learning SIMPLE
$ bigmler --train customer2012.csv
--test new_customers.csv
--objective churn
>>> from bigml.api import BigML
>>> api = BigML()
>>> source = api.create_source("s3://bigml-public/csv/sales.csv")
>>> dataset = api.create_dataset(source)
>>> model = api.create_model(dataset)
$ curl https://bigml.io/model?$BIGML_AUTH
-X POST
-H "content-type: application/json"
-d '{"dataset": "dataset/50ca447b3b56356ae0000029"}'

Agenda
BigML web-based interface (10-15 min)
Questions (10-15 min)
$ bigmler --train customer2012.csv
--test new_customers.csv
--objective churn
>>> from bigml.api import BigML
>>> api = BigML()
>>> source = api.create_source("s3://bigml-public/csv/iris.csv")
>>> dataset = api.create_dataset(source)
>>> model = api.create_model(dataset)
$ curl https://bigml.io/dataset?$BIGML_AUTH
-X POST
-H "content-type: application/json"
-d '{"source": "source/50ca447b3b56356ae0000029"}'
BigML API, API Bindings, BigMLer (5 min)
Challenges (10-15 min)
#1 Machine Learning Breadth and Depth
#2 User Diversity
#3 Simplicity
#4 Scalability
#5 Measuring Impact
#6 Pricing

How it works

BigML Resources
csv, arff, xls
https, s3, azure, odata
Sources local and remote
Datasets
Stream histograms
Statistics
Models
Interactive
Compoundable Random Decision Forests
Actionable: exportable to rules, code, pmml
Predictions
Form-based Predictions
Question by Question
Local predictions
Evaluations
Classification
Regression
Comparison

BigML API

3,500+ users
35,000+ models
BigML

FREE subscription?
mail your username to:
acm@bigml.com

Challenges
#1 Machine Learning breadth and depth
#2 User Diversity
#3 Simplicity
#4 Scalability
#5 Measuring Machine Learning Impact
#6 Pricing

...or you can deal with that!
#1 Supervised learning
#2 Unsupervised learning
#3 Semi-supervised learning
#4 Reinforcement learning
#5 Learning to Learn
#1 machine learning breadth and depth

...or you can deal with that!#1 machine learning breadth and depth

Phrase a problem as an ML task
The stages of an ML application
Data Wrangling
Feature Engineering
Learn from Data
Pre-evaluate
Measure Impact

Problems
Techniques
Applications
Classification
Regression
Clustering
Density Estimation
Manifold learning
Active learning
etc.
Just solving a couple of
problems and using a few
techniques thousands of
applications can be developed
churn prevention, date matching, decision making, diagnostics, fraud
detection, detecting tumors, detecting investment opportunities, human
body pose estimation, pedestrian tracking, predictive analytics,
recommendation systems, risk analysis, spam detection, etc

Understanding
the past
Predicting the
future
Why Trees first?

Why Trees?

A Machine Learning application requires more tasks (that
are even more important) than just learning from data.
Just solving one problem more will enable a huge number of
applications more.
What problem(s) to tackle next and which techniques to
use?

Experts
Aficionados
Practitioner
Newbies
Absolute beginners
#2 user diversity
How to prioritize what to build
next? More features for the
expert or simplifying more for
the newbies?

Time-to-productivity
+
+
Expertise
#2 user diversity

#2 user diversity
MBs PBs
MBs PBs
Actual size
Size
Most users believe their data is much bigger than
it really is

NumberofJobs
+
+
Size of Job
#2 user diversity

#3 simplicity

“Any fool can make something
complicated. It takes a genius to
make it simple.”
― Woody Guthrie
#3 simplicity

‣install
‣configure
‣use
‣train
‣understand
‣test
‣pre-evaluate
‣measure impact
‣deploy
‣scale
‣access programmatically (API)
#3 simplicity
Simple means much more than a easy-to-use interface

#4 scalability
N
CONCURRENT
JOBS
from
1 CUSTOMER
1 JOB
from
1 USER
N JOBS
from
M CUSTOMERS

Infrastructure

#5 measuring machine
learning impact

Measuring “actual” impact is complex and goes
beyond traditional performance evaluation.
Imagine that an algorithm predicts that user Alice is going
to buy a Magic Potion.
‣ But Magic Potions are out of stock.
‣ Should we blame
‣ the algorithm for the “false positive” prediction?
‣ the data scientist for not including that feature?
‣ operations for running out of stock on things that
customers want to buy?
#5 measuring machine learning impact

Kiri Wagstaff, Machine Learning that Matters, ICML, 2012
The stages of an ML research program
Very inspirational!!!

Phrase a problem as an ML task
Data Wrangling
Learn from Data
The stages of an ML application
Feature Engineering
Pre-evaluate
Measure Impact !!!!!

#6 pricing

Pre-pay-as-you-go

Subscriptions

BigML 1-click model
You can deal
with this...
Machine Learning made easy?

BigML 1-click model
You can deal
with this...

Ease-of-use
+
+
2013

Ease-of-use
+
+
2013 2014 2015 2016 2017 2018
Machine Learning made Easy!!!

Questions

Unknown Model
f : X -> Y
Example: ideal credit approval formula
Models
M
Example: set of candidate
credit approval formulas
Learning from Data
Learning
Algorithm
Based on Learning from Data by Y. Abu-Mostafa, M. Magdon-Ismail and H. Lin
Final Model
g ~ f
Example: learned credit
approval formula
Training Examples
(x1, l1), (x2, l2), ..., (xN, lN)
Example: historical records of credit customers
x1
xN
labelf1 f2 fn

A few Challenges to Make Machine Learning Easy

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