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skymind.io | deeplearning.org | gitter.im/deeplearning4j
SKIL - Skymind Intelligence Layer
● Exploratory Data Analysis (EDA)
● Training Model
● Deploy Model
● Monitor model over time (maintenance)
● Scale model as it gets more usage
Enterprise Deep Learning workflows
● Infrastructure for USING Deep Learning
● “Serving” models to end users
● Visualization
● Auditing of data flow (Where did that come from?)
● Bundled hardware acceleration
Training
● Need to visualize
● Neural nets aren’t interpretable
● DL has its own vocabulary in addition to “Machine learning”
● Hard to track research from practical
● Not much emphasis on “apps”
Why is training “hard”?
Training UI
Flow
Feature Extraction
Histograms
● Infrastructure for USING Deep Learning
● “Serving” models to end users
● Visualization
● Auditing of data flow (Where did that come from?)
● Bundled hardware acceleration
Deployment
DC/OS
● Self contained dependencies
● Run on prem or cloud
● Scale independent of cpu or gpu
● “Develop same as production”
Docker
● Docker-compose up
● Dcos install “package”
Usage
After Installation (Monitoring!)
Production Monitoring as well (Conductr)

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