micro architecture for machine learning big data
TRANSCRIPT
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Micro Architecture for Machine Learning/Big Data
Adam Gibson - East Bay JUG Sep 2015
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Me:
CTO and Co founder of Skymind
GU Faculty Advisor
Book Author - Deep Learning: A Practitioner's Approach
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Micro Services
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Benefits/Trade offs
Monolithic - One app Easy to update at first
Microservices - meant for scale, modular components, easier for bigger teams
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Software Development Life Cycle
Starts small
Early - Minimum Viable product/ Move fast break things
Mid stage - Company will actually last - now let’s focus a bit on scale might have growing pains
Late stage - too many cooks in the kitchen needs separation of concerns
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Credit: http://startupquote.com/post/1624569753
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No Silver Bullet - Different stages/sizes
In the SDLC - different incentives for different teams/companies of different sizes
Microservices can also go wrong:http://martinfowler.com/articles/microservice-trade-offs.html
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Takeaway - Do what’s right for your product
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Data Science and ML
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Stats + Software engineering
(Not actually true: We WISH it was)
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Analytics and Products
A/B Testing (does this button increase my revenue/CTR?)
Data/Analytics Products: Think BI + some machine learning depending on the application
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Machine Learning
Given some observations make some inference based on trends in data
Label stuff (supervised learning)
Predict something (regression)
Group stuff (clustering)
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Regression
Given some attributes (features) predict some continuous value
Attributes of house - predict price
Pricing movements in stock market
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Classification
Churn Prediction (Will churn or not churn)Big Spender or not big spammerSpam or not spamFraud or not FraudPicture of? (cat/dog/cow)
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Clustering
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Work flowSome problem needs to be solved
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Exploratory Data Analysis
Extract Transform LoadNormalize (maybe a part of the loading process depending on data warehousing process if any)
Visualize
Determine way to solve problem if any
Get some cursory results
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EDA Cont.
Validate results
Scope out problem
Deploy results
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Parallels to software engineeringBoth have a lifecuylce to follow with different standards for different stages
Data science teams are also similar in that you start by building the data infrastructure adding on the analysis later
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Machine Learning + Software
Machine learning is integrated in to software
Often a second class citizen in engineering standards
Data scientists don’t often think about production
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ML Software
Data pipelines (complete process of data all the way through to modeL) are often messy/adhoc)
To be done right involves 2 + teams of data engineers AND scientists
Data engineers don’t often know much ML and scientists don’t know much about software infrastructure
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Components
ETL/Vectorization
Data Manipulation
Data Exploration
Model building
Model integration with serving system (lambda architecture)
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Should be separated
ETL not tied to machine learning models
Everything should be swappable
Should run interchangeably on different platforms
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Deeplearning4j
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Questions? [email protected]