This document discusses analyzing video data with GraphLab Create. It introduces Dato's products for ingesting, transforming, modeling and deploying machine learning models on unstructured data like images, text, graphs and tabular data. It then outlines a demo of using computer vision and face recognition techniques to match actors' faces from movie frames to subtitles and screenplay text. Instructions are provided for installing GraphLab Create and links shared for additional resources.

1. Dato Confidential1
Analyzing Video with GraphLab Create
June 16, 2016
Guy Rapaport, Data Scientist, Dato EMEA
guy@dato.com

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Dato: We Intelligent Applications

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Some of our Customers
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Business
must be intelligent
Machine learning
applications
• Recommenders
• Fraud detection
• Ad targeting
• Financial models
• Personalized medicine
• Churn prediction
• Smart UX
(video & text)
• Personal assistants
• IoT
• Socials networks
• Log analysis
Last decade:
Data management
Now:
Intelligent apps
?
Last 5 years:
Traditional analytics

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Creating a model pipeline
exploration
data
modeling
- Images
- Text
- Graphs
- Tabular Data

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Creating a model pipeline
Ingest Transform Model Deploy
Unstructured Data

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Creating a model pipeline using Dato products
Ingest Transform Model Deploy
Unstructured Data
SFrame Engine
(FREE, open
source)
GraphLab Create
(Scalable Machine
Learning Python
Library,
4K/machine/year)
Predictive Services
(Serving + Load Balancing
+ AB Testing,
10K/machine/year)

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$ pip install –U graphlab-create

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What will we cover today?
1. Match a movie’s screenplay with its subtitles.
- Now we know who says what and when.
2. Extract frames, then actors’ faces, from the movie.
- We’ll use opencv for video manipulation and face detection.
3. Train a face recognition model over the faces.
- What’s the smallest portion of the movie we can get good
results from?
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Python vs. Anaconda
• You can download Python for free from python.org .
- Python with its standard library.
• Or, you could download the Anaconda distribution.
- Python + tons of installed packages + package managers.
• It’s the same Python, but Anaconda includes both pip and
also with it’s own package manager, conda.
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pip vs. conda vs. virtualenv
pip – install Python packages.
conda – install Python packages + any OS packages required
for your package to work (libraries etc).
$ conda install -c menpo opencv3=3.1.0
virtualenv – separate environment (by manipulating the
$PYTHONPATH etc.) so packages won’t break.
You can have multiple Python versions on the same machine,
and use a Python version in different environments.
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Look Deeper!
1) Building a Face Recognition System with OpenCV in the blink of an Eye
• https://github.com/rragundez/PyData
• Live video from webcam, online analytics
2) Using mxnet for deep feature extraction
• https://github.com/dmlc/mxnet/blob/master/example/notebooks/predict-
with-pretrained-model.ipynb
• mxnet is now integrated into GraphLab!
3) mxnet-face
• https://github.com/tornadomeet/mxnet-face

14. Dato Confidential
Confidential – Dato internal use only. ©2015 Dato, Inc.
Questions?
“For the purpose of learning the Answer to the
Ultimate Question of Life, The Universe, and Everything,
the supercomputer Deep Thought was specially built.
It takes Deep Thought 7½ million years to compute and check the
answer, which turns out to be 42. Deep Thought points out that
the answer seems meaningless because
the beings who instructed it
never actually knew what the Question was.”
- Douglas Adams, “The Hitchhiker’s Guide to the Galaxy”

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Our Machine Learning Specialization
in Coursera
https://www.coursera.org/learn/ml-foundations

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Confidential – Dato internal use only. ©2015 Dato, Inc.
Thanks!
Install using pip: $ pip install -U graphlab-create
Dato Launcher Download:
https://dato.com/download/
The benchmarks on GitHub:
https://github.com/guy4261/glc_pagerank_benchmark
Coursera Course:
https://www.coursera.org/learn/ml-foundations
Reach out: guy@dato.com