Intelligent apps are emerging as the next frontier in analytics and application development. Learn how to build intelligent apps on MongoDB powered by Google Cloud with TensorFlow for machine learning and DialogFlow for artificial intelligence. Get your developers and data scientists to finally work together to build applications that understand your customer, automate their tasks, and provide knowledge and decision support.

1. Building Intelligent Apps
with MongoDB and
Google Cloud

2. JANE FINE
Director of Product Marketing,
Analytics
@janeuyvova

3. Analytics is a Constant Underachiever
2000
Business Intelligence
Data Warehouse, OLAP, Ad-
hoc, Reporting, Dashboards
Big Data
Hadoop, MapReduce,
Predictive, ML, Real-time
2010
AI
Intelligent Things, IoT,
Systems of Engagement
2017
“In 2018, 75% of AI projects will underwhelm because they fail to model
operational considerations, causing business leaders to reset the scope of AI
investments.”
FORRESTER.COM/PREDICTIONS

4. $40B+ eCommerce
platform
experience of millions of
mobile gamers
metadata for every single
item for sale on
eBay.com
world’s leading design
collaboration platform
20M+ users
$150B+ traded
reinvent travel for
millions of
customers
lab and clinical analysis for
innovative medicines
personal and business
finance management
worldwide
Applications Change Our World

5. Operational
AI
ML
What We Set Out To Do
shop for
products
online
Intelligent App
get personalized
product
recommendatio
ns
shop on
mobile
device

6. What’s an Intelligent App?
Applications are increasingly combining real-time analytics,
machine learning and AI to provide understand the customer,
automate their tasks and provide knowledge and decision support

7. Why Is This Hard?
Intelligent
App
Developer
s
Data Scientists
uses: live data
guided by: user
stories
produces:
functionality
uses: prepared data
guided by: question
produces: insight
RELEASE DEFINE
BUILD
AGILE
DATA
PREP
BUILD
MODEL
GET
INSIGH
T
VIZ
DEPLOY
TRAIN/EVA
L

8. eCommerce App:
SwagStore

9. eCommerce App: SwagStore
Get notified when a
sold-out item is
restocked
Browse for your favorite
MongoDB swag
Put items in cart and
checkout
View your orders

10. MongoDB Stitch Serverless Platform
Streamlines app development with
simple, secure access to data and
services from the client with
thousands of lines less code to
write and no infrastructure to
manage.
Getting your apps to market faster
while reducing operational costs.

11. SwagStore: How We Built It
UI components
Routes
Application Flow Control
Google
Authentication
Twilio Notifications
Functions
Rules
Triggers
Service Integrations
Flexible Document
Model
Easy to Work With Data

12. DEMO:
SwagStore

13. SwagStore + RecEngine

14. Intelligent eCommerce App: SwagStore +
Receive personalized
product
recommendations based
on ML algorithm
Recommendatio
n Engine

15. Intelligent SwagStore: How We Built It
SwagStore
Google Cloud ML Trains and
Tunes Model
TensorFlow WALS Algorithm
Google Cloud Endpoints
Serves Recommendations
Stitch Initiates
Recommendations
developer data scientist developer
data engineer

16. Production Recommendation Solution on
GCP
Google Analytics
BigQuery
Google Analytics
360
Customer Web
Application
Web
Server
Application
Server
Database
Server
Rec API
App Engine
Cloud
Endpoints
Model Training
Cloud Machine Learning
Orchestration
Cloud Composer
ML Data
Training
Model files
Browser
Client
Mobile /
Tablet Client

17. The Recommendation API
Google Analytics
BigQuery
Rec API
App Engine
Cloud
Endpoints
Model Training
Cloud Machine Learning
ML Data
Training
Model files
UserID
User factor
x Item
factors
Item ID &
User ID
Maps
Sort & Filter
Article
Index
List
Item ID &
User ID
Maps
Article
ID
List
ML Model

18. Training Recommendation Model
1. install the model code
2. place data into your Cloud Storage Bucket
3. run training script
When the training is finished, the model data is saved in a subdirectory named
model under the job directory of the training task. This data consists of several
arrays, all saved in numpy format
./mltrain.sh train
gs://recserve_jfmlrecengine/swag_pageviews.cs
v --data-type web_views

19. Tuning Recommendation Model
Hyperparameter tuning optimizes your machine learning model for most accuracy
Typically data scientists experimenting with various values, test the model, and then pick a
combination of parameters with the best performance.
But you can test every possible combination of parameters…. it would take a very very long
time
● Each hyperparameter is passed to
the hyperparameter tuning job on
Cloud ML Engine.
● The model writes a TensorFlow
summary with a special tag that's
set to the metric that evaluates the
quality of the model.
● This summary metric enables the
search process of the Cloud ML
Engine hyperparameter tuning
service to rank the trials.
./mltrain.sh tune
gs://recserve_jfmlrecengine/
swag_pageviews.csv --data-
type web_views

20. AI does AI
Systematic exploration
of the model space, using
the techniques finessed in
AlphaGo, yields super-human
performance in AI network design

21. Generate Recommendations
model.py : generate_recommendations
input
user: row index of the user in the rating matrix
items: list of indexes for items that the user has rated / viewed
latent factors: row and column factors generated by training / tuning the
model
number of desired recommendations
https://jfmlrecengine.appspot.com/recommendation?us
erId=5448543647176335931&numRecs=6
{"articles":
["299824032"
,"299935287"
,"299865757"
,"299959410"
,"298157062"
,"299816215"
]}

22. Serving Recommendations with Stitch
MongoDB Stitch send a HTTP GET
request to Google Cloud Endpoint to
obtain recommendations
For user who is logged in
Get list of product
recommendations
Update user profile with that list
Service Integrations make it
simple for your app to use
leading third party services
Functions: build complex logic and orchestrate
data between clients, and services
Stitch scales precisely to meet your usage.

23. DEMO:
SwagStore + RecEngine

24. SwagStore + ChatBot

25. Intelligent eCommerce App: SwagStore +
Shop for products via
chat interface
AI Chatbot

26. Intelligent SwagStore: How We Built It
SwagStore
Google DialogFlow:
Intent, Entities, Webhooks
Stitch - Intent Fulfilment Slack - Front End
developer
data scientist
developer data engineer

27. DialogFlow + Stitch Architecture
Stitch HTTP
Service
Webhook
Stitch Functions to
retrieve products
from MongoDB

28. Me: “Can you help me find a jacket?”
ChatBot: “What color would you like?”
Me: “White, please”
ChatBot: “I found you a white Egmont Jacket”
DialogFlow: Rich and Natural
Conversational Experiences
Stitch Service
Integration

29. Enabling DialogFlow Fulfillment With Stitch
Stitch security enabled by default

30. Enabling DialogFlow Fulfillment With Stitch
return item from SwagStore Catalog to
DialogFlow
{
"fulfillmentText": "I found you a white
Egmont Packable Jacket. Check it out here:
https://mdb-swag-
store.netlify.com/products/299824032"
}
perform $find given
parameters requested by
DialogFlow: product type
and product color

31. DEMO:
SwagStore + ChatBot

32. Working Together
Developer
s
Data Scientists
DATA
PREP
BUILD
MODEL
GET
INSIGH
T
VIZ
DEPLOY
TRAIN/EVA
L
RELEASE DEFINE
BUILD
AGILE

33. Build. Plug In. Innovate.
Intelligent App

34. Resources and Credits
MongoDB Stitch:
https://www.mongodb.com/cloud/stitch
Google Cloud ML:
Tutorials: https://cloud.google.com/solutions/machine-
learning/recommendation-system-tensorflow-train-cloud-ml-engine
GitHub: https://github.com/GoogleCloudPlatform/tensorflow-
recommendation-wals
DialogFlow:
https://dialogflow.com/docs/getting-started
Special thanks to Aydrian Howard, Jesse Krasnostein, Mike Lynn, Drew DiPalma, Avery Rosen,
Shawn McCarthy, Seong Park... Lukman Ramsey & Asher Halpert