The document discusses building intelligent applications using MongoDB and Google Cloud, highlighting the integration of machine learning and real-time analytics to enhance user experiences. It outlines the development of an intelligent ecommerce app named Swagstore, which utilizes recommendation engines and chatbots for personalized shopping. It also details the processes involved in training machine learning models, serving recommendations, and integrating various services to streamline app development.

1. Building Intelligent Apps with
MongoDB and Google Cloud

2. Jane Fine
Director of Product Marketing, Analytics
jane.fine@mongodb.com
@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
ecommerceapp
shop for
products
online
Intelligent App
mobileshoppingchatbot
get personalized
product
recommendations
shop over
MMS/WhatsApp
on mobile device
rec engine
ML

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
Developers
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
INSIGHT
VIZ
DEPLOY
TRAIN/EVAL

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. Stitch: MongoDB 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
Recommendation
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.csv
--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

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?user
Id=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/pro
ducts/299824032"
}
perform $find given
parameters requested
by DialogFlow: product
type and product color

31. DEMO:
SwagStore + ChatBot

32. Working Together
Developers
Data Scientists
DATA
PREP
BUILD
MODEL
GET
INSIGHT
VIZ
DEPLOY
TRAIN/EVAL
RELEASE DEFINE
BUILD
AGILE

33. Build. Plug In. Innovate.
Intelligent App

34. Resources and Credits
Aydrian Howard, Jesse Krasnostein, Mike Lynn, Drew DiPalma, Avery Rosen, Shawn
McCarthy, Seong Park...
Lukman Ramsey & Asher Halpert
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

35. Thank You!