https://253bb1695cca2a388faddf6cd4.doorkeeper.jp/events/50918

1. skymind.io | deeplearning.org | gitter.im/deeplearning4j
SKIL - Skymind Intelligence Layer

2. ● Exploratory Data Analysis (EDA)
● Training Model
● Deploy Model
● Monitor model over time (maintenance)
● Scale model as it gets more usage
Enterprise Deep Learning workflows

3. ● Infrastructure for USING Deep Learning
● “Serving” models to end users
● Visualization
● Auditing of data flow (Where did that come from?)
● Bundled hardware acceleration
Training

4. ● 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”?

5. Training UI

6. Flow

7. Feature Extraction

8. Histograms

9. ● Infrastructure for USING Deep Learning
● “Serving” models to end users
● Visualization
● Auditing of data flow (Where did that come from?)
● Bundled hardware acceleration
Deployment

10. DC/OS

11. ● Self contained dependencies
● Run on prem or cloud
● Scale independent of cpu or gpu
● “Develop same as production”
Docker

12. ● Docker-compose up
● Dcos install “package”
Usage

13. After Installation (Monitoring!)

14. Production Monitoring as well (Conductr)