In the last 10 years, workplaces have witnessed an explosion of highly specialized applications, in which a typical mid-size company may have data locked up in hundreds of applications. For data scientists, this has led to both challenges integrating data from increasingly siloed sources as well as difficulties deploying AI and ML applications in a fragmented IT landscape. In this talk, we demonstrate how data scientists solve this problem through application networks, which emerge when applications are orchestrated through APIs. We provide an end-to-end example with MuleSoft and Tensorflow, showing how data scientists can tap the data in an application network to train models and then deploy models as intelligent applications back into the network.

1. WITH MULE AND TENSORFLOW
INTELLIGENT APPLICATION NETWORKS

2. MACHINE LEARNING BASED AI WILL TRANSFORM 80% OF BUSINESSES IN FIVE YEARS
https://news.greylock.com/the-new-moats-53f61aeac2d9 https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/

3. MACHINE LEARNING BASED AI WILL TRANSFORM 80% OF BUSINESSES IN FIVE YEARS
https://news.greylock.com/the-new-moats-53f61aeac2d9 https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/

4. A MAJOR CHALLENGE IS MANAGING CONNECTIVITY AND CONSTANT CHANGE
Accelerating hyperspecialization of
data, applications, and devices

5. AVERAGE ENTERPRISE HAS 91 CLOUD SERVICES JUST IN MARKETING GREW FROM 150 TO 6800 SINCE 2011
HYPER SPECIALIZATION IN MARKETING
https://chiefmartec.com/2018/04/marketing-technology-landscape-supergraphic-2018/

6. THIS TREND IS ACCELERATING IN EVERY INDUSTRY AS THE PACE OF CHANGE INCREASES
COMPANIES DOING “INITIAL COIN OFFERINGS ”
https://www.cbinsights.com/reports/CB-Insights_Blockchain-In-Review.pdf

7. TO BUILD A SYSTEM OF INTELLIGENCE, START WITH AN “MINIMUM VIABLE MODEL” AND ITERATE
DATA TEAMS MUST ITERATE QUICKLY
Identify, Refine Problem
Deploy System
Collect Feedback
Improve Model
Train Model
Prepare training data
Collect Data
Explore Data
Evaluate Model

8. MOST TIME-CONSUMING, LEAST ENJOYABLE DATA SCIENCE TASK
COLLECTING AND CLEANING DATA IS 80%
60%
4%
9%
19%
3%
5%
https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says
Creating Training Set
Cleansing and organizing
Collecting Data
Mining for patterns
Other
Refining Algorithms

9. WITH HYPERSPECIALIZATION CURRENT PROBLEMS WILL BECOME WORSE
▸ Custom code to get models into
production grade applications
▸ Lack tight integration with user
▸ Long ETL cycles, different update cycles
▸ Poor discoverability
▸ Lack of labeled data
▸ Data lakes become dumping grounds
▸ Governance
▸ Poor metadata
FAILURE TO ITERATE RAPIDLY

10. CONNECTIVITY AND ORCHESTRATION MEETS MACHINE LEARNING TO ENABLE RAPID ITERATION
SYSTEMS OF INTELLIGENCE

11. CONNECTING APPLICATIONS, DATA, AND DEVICES INTO COMPOSITE APPLICATIONS
APPLICATION NETWORKS
▸ APIs and metadata
▸ Data types
▸ Capabilities
▸ Orchestration
▸ Composite applications
AWS
https://www.mulesoft.com/resources/api/what-is-an-application-network

12. RAPIDLY ITERATE PRODUCTION MODELS AS SERVICES
Define Model
Train Params
Wrangle
Pipeline Orchestration Composite Application
Update API
Build App
Publish API
Engage
Deploy as Microservice
Evaluate and Deploy
Connect
Collect Feedback
Mule Connectors
Connect Real-Time

13. A CHURN PREDICTION: DESIGN AND IMPLEMENT AN API
API Spec and Type Definitions (RAML)
Querying the production server in Postman

14. ONCE YOU DEPLOY AND PUBLISH, YOU’VE ADDED A NOTE TO THE APPLICATION NETWORK
Publish the API for other applications to use in
MuleSoft’s Anypoint Platform
Deploy as a micro service to MuleSoft’s PaaS (based on Kubernetes)

15. LOAD KERAS MODEL AND WEIGHTS INTO SPARK AND CALL FROM MULE

16. ORCHESTRATING THE ML PIPELINE WITH MULE FLOWS
Evaluate
R Packages
• Yardstick
Mule
• Scatter-Gather
• Choice
Train
R Packages
• Keras
• Rsampler
Tensorflow
• 35 inputs
• 2 hidden layers
• Binary output
Fetch
Mule
Connectors
Preprocess
Mule
• Scheduler
• DataWeave
R Packages
• Recipe
• Dplyr
• Farcats
Publish
Persist
• Recipe
• Model
• Weights
Mule
Connector

17. BUILDING THE ML PIPELINE IN MULESOFT’S ANYPOINT STUDIO
R is invoked here

18. PROCESSING THE DATA USING RECIPES
Adapted from: https://tensorflow.rstudio.com/blog/keras-customer-churn.html
Data Set
Watson dataset on Telco Churn
•20 predictors of churn
•Demographics from Salesforce
•Products from billing database
•Transactions from S3 flat files
Preparing the training and test with R Recipes

19. TRAIN THE MODEL WITH KERAS WITH TENSORFLOW BACKEND
Diagram here
Defining the model in R Keras
HIDDEN LAYER 1
(20, 16), RELU
HIDDEN LAYER 2
(16, 16), RELU
HIDDEN LAYER 2
(16, 1), SIGMOID
Training the model in R Keras

20. EVALUATE WITH YARDSTICK, “PUBLISH” UPDATE MODEL IF IT IMPROVES

21. ADDING SYSTEMS OF INTELLIGENCE TO THE APPLICATION NETWORK
INTELLIGENT APPLICATION NETWORKS
▸ Specifications
▸ Capabilities
▸ Ontologies
▸ Orchestration
▸ Composite applications
▸ Knowledge Graph
▸ Systems of Intelligence
▸ Computational Graphs
AWS
TF
TF
TF
TF

22. LINK TO PRESENTATION, VIDEO OF DEMO, AND CODE FOR EXAMPLE
Soren Harner is a principal data scientist with Permaling.
Permaling provides strategy consulting, implementation, and
training for AI in the enterprise.
https://permaling.com
soeren [at] permaling [dot] com
https://github.com/sharner/sdss-mule
Permaling