Quick overview of the latest in big data and artificial intelligence. A lot of buzzwords being thrown around, hopefully this presentation will demystify many of the terms.

1. October 2016
Predictive Analytics
Big Data & Artificial Intelligence

2. Agenda
Artificial Intelligence AI
Big Data
Machine Learning
Deep Learning
Neural Networks
NLPNatural Language Processing
Demystify the following buzzwords.
Image Recognition
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3. Ultimate Goal: Predictive Analytics
Predict what users will want to buy.
A consumer searches
for a TV and based on
previous customers
data, show a product
that has a high
probability of being
bought as well.
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4. Evolution of Data Analytics
1990s 2000s
Excel Business Intelligence (BI)
Dashboards
2015 and beyond
Actionable
Insights
What Happened? What’s Happening? What Will Happen?
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5. The Process
Structured and
unstructured (ex.
video) data
Data is stored in
databases and
servers
Data
Generated
Data
Stored
Actionable
Insights
Data
Processing
Process the data
using CPU/GPUs
and AI algorithms
to detect patterns
Predictive
signals are
generated
Central Processing Unit (CPU) / Graphics Processing Unit (GPU)
Big Data Artificial Intelligence
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6. How Did We Get Here?
Databases
(the 80s)
Data Warehousing
(the 90s)
• Relational databases
• Gigabytes in size
• Low latency
• Terabytes in size
• Custom hardware
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7. Today, it’s Big Data
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8. Artificial Intelligence (AI)
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9. Artificial Intelligence (AI)
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10. When To Use Machine Learning
A pattern exists1
We cannot pin down the pattern
mathematically
2
We have data and hopefully lots of
data
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11. Types of Machine Learning
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12. Supervised Learning
X
X
X
X
X
Price
Square Feet
We know what we are trying to
predict. We use some examples that
we and the model know the answers
to “train” our model. It can then
generate predictions to examples we
don’t know the answer to.
Example: Predict the price of a house
based on the size of the house.
X
X
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13. Unsupervised Learning
O
O O
O
O
O
O
OO
O
X
Y
OO
O O
O
We don’t know what we are trying to
predict. We are trying to identify
some naturally occurring patterns in
the data which may be informative.
Example: Try to identify “clusters” of
customers based on the data we have
on them.
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14. What is Deep Learning?
• Deep Learning and Neural Networks are synonymous
• It’s a branch of machine learning based on a set of algorithms that
attempt to model high level abstractions in data by using a deep graph
with multiple processing layers, composed of multiple linear and non-
linear transformations
What we see What the computer “sees”
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15. Tools of The Trade
Apache SystemML
Google Cloud
Machine Learning
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16. mrjain@gmail.com
Questions?
version: draft

17. Appendix
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18. AI Researchers
Geoffrey Hinton
University of Toronto
Google
Yoshua Bengio
University of Montreal
Yann LeCun
New York University
Facebook
Andrew Ng
Stanford University
Baidu
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19. CPU vs GPU Performance
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20. MapReduce
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21. The Name…Hadoop
Named after the yellow toy elephant of Doug Cutting’s son.
In 2006 while working at Yahoo, Doug came up with the Hadoop
framework. In 2008, it was taken over by the open source group
Apache, hence the official name is Apache Hadoop.
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22. Hadoop to the Rescue
“an open source framework written in Java for storing and
processing massive amounts of data in a distributed manner”
1
Hadoop Distributed File System
(HDFS). Scalable file system that
distributes and stores data across
many machines in a cluster.
MapReduce – framework for
distributed processing.
2 Key Components of the Framework:
Storage 2 Analysis
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23. Hadoop Architecture
Hadoop can run on cheap commoditized
hardware on premise or in the cloud.
Stores files in large
blocks (64MB) across
multiple machines for
fault tolerance. By
default, data is stored
on 3 separate machines
HDFS
MapReduce
Breaks large data processing
problems into multiple steps,
namely Mappers (DataNode)
and Reducers (TaskTrackers)
that can be worked on in
parallel on multiple machines
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24. MapReduce Store Sales Data
(100MB)
Mappers Name Node 1 Data Node 1
(64MB)
Data Node 2
(36MB)
LA NYC LA NYC
Reducers Job Tracker Task Tracker
1
LA LA
Task Tracker
2
NYC NYC
Shuffle and Sort
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25. MapReduce
Map Shuffle & Sort Reduce Result
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26. Hadoop 1.0 vs 2.0
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27. The Future…
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