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)