Nexxworks bootcamp ML6 (27/09/2017)

Nexxworks Bootcamp Ghent - 27/09/2017 karel.dumon@ml6.eu - @kareldumon

Nexxworks Bootcamp Ghent - 27/09/2017
Building machine learning* solutions
with Google Cloud*

Team of Data Engineers, Data Scientist & Machine
Learning Engineers
Closing the gap between lots of data - lacking insights
Robust & agile ML solutions through scalable APIs
Premium Partner of Google Cloud

Rise of ML
Cases
The Future
Get started!

Confidential & ProprietaryGoogle Cloud Platform 8
2012 20132002 2004 2006 2008 2010
GFS
MapReduce
Bigtable Colossus
Dremel Flume
Megastore
Spanner
Millwheel
Pub/Sub
F1
2016
Dataflow
TensorFlow
Google’s 15+ years innovation in data

Decrease the innovation gap
2012 20132002 2004 2006 2008 2010
GCS
Dataproc
Bigtable GCS
BigQuery Dataflow
Datastore
Spanner
Dataflow
Pub/Sub
F1
2016
Dataflow
Cloud ML
NoSQL

11
Rapidly Accelerating Use of Deep Learning at Google
Number of projects using some form of deep learning
2012 2013 2014 2015
1500
1000
500
0
Used across products:

What’s going on?
The cloud is very good at
handling, storing and manipulating
large volumes of data.
What we really care about now is
understanding the data.

MACHINE LEARNING
MACHINE LEARNING EVERYWHERE

Keys to Successful ML
Large Datasets Good Models Lots of Computation

15
The Deep Learning Revolution: ImageNet 2012
Deep Learning on Google Trends

Nexxworks Bootcamp Ghent - 27/09/2017 16

The old, algorithmic approach
“apple”
“orange”
“banana”
IF (round) THEN
IF (orange AND coarse) THEN
“orange”
ELSE IF (green AND smooth) THEN
“apple”
ELSE IF ...
...
ELSE IF …
“banana”

Let the machine find the rules
“apple”
“orange”
“banana”
?

Confidential & Proprietary
ConvNets

+ ‘The Next Big Thing’
+ Sentient AI in the next 10 years
(‘The Singularity’)
+ Will put humans out of a job
+ Foolproof
MACHINE LEARNING
in popular culture

+ Been around for 60 years now
+ ‘Sentient next year’, every year,
for the last 60 years
+ AI winters: 1970, 1990, … ?
+ Not foolproof
MACHINE LEARNING
reality

24
A person on a beach
flying a kite.
A person skiing down a
snow covered slope.
A group of giraffe standing
next to each other.

25
A woman riding a horse
on a dirt road.
An airplane is parked on the
tarmac at an airport.
A group of people
standing on top of a
beach.

Use cases to illustrate
what we actually do.

Get inspired,
but let’s start simple.

Logistics optimisation
Predicting the time packages stay in customs
+ Better predict airplane cargo loads
for an international courier
+ For each parcel, predict the time
under inspection by customs
(0 min = not picked up)
+ Better prediction → Better planning
Better planning → Fuller cargos
Avoid sudden overload

Training the deep learning network (simplified)
A parcel’s
attributes
(weight, size, text,
colours, origin ...)
The predicted
package delay
in customs
>100k examples
Full case: 3 days!

Predictive Maintenance: towards a highly dynamic Digital Twin
Design Connect Predict
theoretical model collect data create Digital Twin
Testing generates theoretical
life-time model
- in-house testing
- remaining life-time in
‘ideal conditions’
Machine Learning model
predicts expected failure time
Client receives personalized
model by including custom
environmental factors
- usage, temperature,
humidity…
Cloud computing/storage
STATIC MODEL HIGHLY DYNAMIC ML MODEL
connected device
Digital Twin
ML

37
Automated Customer service
Building the smartest bots

38
What did we do?
+ Shipped state-of-the-art system in under one month
turnaround time
+ System is not an ‘Artificial Intelligence’
+ System outperforms most solutions currently marketed as AI
Why use Google Cloud Platform?
+ Access to massive computational resources to train our models
+ Able to dynamically adjust to changing number of requests
+ Leverage Google powered API’s
The solution

46
The solution - semantic knowledge

49
The solution - context understanding

50
Convolutional Neural Network

52
LSTM
“How” “many”
Word2Vec embedding
“minutes” “left.”...
Topic
Long Short Term Memory (LSTM) network

57
Entity Extraction
● “I got charged 7 dollars for a Christmas Card
that I never ordered. Plz remove this from my
invoice, my telephone number is 472867486.”
● “My internet connection is no longer working. I
have ADSL smart-50 and my address is NY
city 52 A on 21st.”
Entity Extraction Postprocessing
7 dollars $7.00
Christmas Card Christmas-card
472867486 +32472867486
ADSL smart-50 ADSL smart 50
NY city 52 A on 21st 52A, 21st avenue, New
York

Other ideas?
smart cities? (garbage detection, road conditions, mobile camera’s for anomaly detection...)

Medical imaging diagnostics with deep learning
Input Output

MEDICAL IMAGE CLASSIFICATION
TECHNICAL DETAILS - DATA
Camelyon16: ISBI challenge on cancer metastasis detection in lymph node
Training & Evaluation: 110 tumor slides, 160 normal slides, 130 evaluation slides
Task 1: whole-slide level prediction binary classification problem
Task 2: find metastasis location segmentation problem

TECHNICAL DETAILS - CHALLENGE BREAKDOWN
Not just another standard image classifier…
Network architecture
Training set construction
Computing environment
Post-processing (classification and segmentation)

TECHNICAL DETAILS - TRAINING
Patches are used to train a deep learning model
- started with simple CIFAR10-model
- ended up retraining Inception-v3 to experiment with synchronous updates across
multiple GPUs
Metastasis patches
Normal patches
Deep Learning model
e.g. Inception-v3

TECHNICAL DETAILS - MULTIPLE GPUs
Multiple GPUs were used to calculate model
updates (based on TensorFlow tutorial)
- individual model replica on each GPU
- all GPUs process a batch of data and send
update of model parameters to CPU, which
performs a synchronized update
Approach was extended to Cloud ML Engine GPUs
during an external ackathon to build a dermatology
POC in 1 day (by starting from a.o. pretrained VGG16)

69
What about
Reinforcement
Learning?

70
Supervised: need large amounts of
annotated training data
Static inference machines
Bad transfer learning capabilities to
new tasks

72
Third person imitation learning

79
Exploration vs Exploitation

Use reinforcement learning to optimize energy usage
Reinforcement learning allows for automatic parameter optimization:
- e.g. energy optimization: let AI-agent control settings to optimize
energy consumption in data center cooling
- See also Google Deepmind

Ultimate Machine Learning with Google Cloud 82

Fleet optimization w/ online reinforcement learning

Advantages that AlphaGO can leverage
1. Fully deterministic: no noise in the game
2. Fully observed: each player has complete information and there are no
hidden variables. (unlike Poker for example)
3. Discrete action space
4. Each game is relatively short (approximately 200 actions)
5. Target function is clear (win/lose) & fast to evaluate
6. Huge datasets of human gameplay are available to bootstrap the
learning, so AlphaGo doesn’t have to start from scratch

“If a top 1% CEO today
understands how software applications get built and
how that changes the way she/he manages,
a top 1% CEO in the near future
will understand how models get built and
how that changes the way she/he builds her/his organization.”
James Cham, Bloomberg Data

1. Educate yourself
2. AI strategy
3. TEST!
(Get your hands dirty)

How?

Plenty of problems to solve...

Tensorflow Training
THANKS!
What my friends think I do
What other computer
scientists think I do
What society thinks I do
What mathematicians think I do What I think I do What I actually do

Nexxworks bootcamp ML6 (27/09/2017)

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