Building Intelligent Apps
with MongoDB and
Google Cloud

JANE FINE
Director of Product Marketing, Analytics
@janeuyvova

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

Applications Change Our World
$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

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

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

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

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

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.

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

Intelligent eCommerce App: SwagStore +
Receive personalized
product recommendations
based on ML algorithm
Recommendation
Engine

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

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

Tuning Recommendation Model
Hyperparameter tuning optimizes your machine learning model for most accuracy
Typically data scientists experimenting with various values, testing the resulting performance of the
model, and then picking 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 as an
argument 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

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
?userId=5448543647176335931&numRecs=6
{"articles":
["299824032",
"299935287",
"299865757",
"299959410",
"298157062",
"299816215"]
}

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

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

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

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

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 Our ChatBot understands and responds to
requests such as:
Stitch Service
Integration

Enabling DialogFlow Fulfillment With Stitch
Stitch security enabled by default

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

Stitch Makes this Easy
Intelligent App
Developers
Data Scientists
RELEASE DEFINE
BUILD
AGILE
DATA
PREP
BUILD
MODEL
GET
INSIGHT
VIZ
DEPLOY
TRAIN/EVAL
NEED NEW STITCH COLLATERAL

Build. Plug In. Innovate
Intelligent
App
RELEASE DEFINE
BUILD
AGILE

Close
you use this methodology to build an intelligent app → chances are you
are already being tasked to do that as developers
use stitch to consume services, like GCP or others
you don’t need to learn NLP, or build from scratch
finally get analytics to drive value, utilize your data scientist
get your analytics to change to world, not just your app...

Resources and Credits
MongoDB 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

Title and bullet content slide
• Bullet level one
• Bullet level two
• Bullet level three

Thesis (long)
Analytics is a constant underachiever. Elusive, shiny and never delivers. Analytics has been positioned for decades as the next frontier that
will help companies gain competitive advantage and drive the top line growth but it always seems to fall short. Generations of analytics
solutions: BI (2000), dashboards (2005), data science (2010), machine learning (2015)… now AI (2017).
Applications on the other hand provide real, measurable value. They have fundamentally changed how we do things. We interact with them
daily and companies we buy products and services from run their business on applications. It’s visible, measureable, proven. Full stop.
How can we make analytics more impactful? How can you make analytics finally deliver something meaningful the way applications already
have? By embedding your analytics into something your customers use every day. Like your app. Enter intelligent apps.
MongoDB can help you create intelligent apps where operational and analytic workloads merge for the benefit of end customer. MongoDB is
creating tools and approaches that are native to your data and its shape. You can finally do something meaningful with the insight your data
scientists find - use it to capture and delight your customers!
So what: make your data science meaningful, make your developers and data scientists work together and make them both more productive;
build more impactful and interesting applications, delight your customers

abstract
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.
enables data scientists to build and bring superior machine learning models to production. Google DialogFlow
helps developers build natural and rich conversational experiences quickly and easily to enage customers in a
new way.

general feedback - dry run 1
show more code
make it obvious: 1, 2,3 - especially around building the model
talk about getting devs and data scientists to work together
twiggle.com - check this out
need to set intelligent apps in the context - forrester, g2crowd are
talking about them, use examples of companies using AI… but maybe
you are not a google, or an ebay - how do you make this easy ? use
this methodology; don’t need to learn NLP, build from scratch

Outline
Setup: analytics contact underachiever/ apps change the world / data
scientists are frustrated / both devs and data scientists cost $$$!
building blocks:
first you have ecommerce app
then you have ecommerce app + rec engine
then you enable you users to shop for products via chat
then you push recommendations back to cloud ML
how to we make this easy
how to we get dev and data scientists to work together

Title and bullet two content slide

Short title with
caption and photo
or graphic slide
A caption or a quote for a photo, logo, graphic,
social medial screenshot, etc. can go here.

Short title with
caption and content
slide
A caption or subhead for the right content can
go here. Content can be table, chart, diagram,
smart object, photo or multimedia.

Title and table slide
Column 1 Column 2 Column 3 Column 4 Column 5 Column 6

Sample single column code slide
{ "_id" : 5,
"line_items" : [
{ "id": 123,
"title": "USB Battery",
"price": 15.0,
"notify_when_ship": [{"name": "eliot", "phone": "123-456-7890"}],
"shipped": true },
{ "id": 512,
"title": "Hip T-shirt",
"price": 45.0,
"shipped": false }
]
}

Sample two column code slide
orders:
{
_id: 5,
line_items : [
{ id: 123,
title : "USB Battery",
price: 15.0 },
shipped: true
{ id: 512,
title : "Hip T-shirt",
price : 45.0,
shipped: false }
],
}
db.orders.update(
{ _id: 5 },
{ $mul: {
"line_items.$[li].price":
0.8}},
{arrayFilters:[
{"li.shipped":{$ne:true}}
]}
)

Application
Driver
Query Router Query Router•••
Query Router
Query Router
Query Router
Query Router
Query Router
Query Router
Query Router
Query Router
Query Router
Query Router
Query Router
Query Router
•••
Shard 1 Shard 2 Shard 2 Shard N

Active/active data center
Secondary – A Secondary – B Secondary – C
Secondary – B Secondary – C Secondary – A
Data Center – EastData Center – Central
Arbiter – B Arbiter – CArbiter – A
Data Center – West
Primary – B
Secondary – B
Primary – A Primary – C
Secondary – C Secondary – A

Active/standby data center
Data Center – West
Primary – BPrimary – A Primary – C
Secondary – B Secondary – C Secondary – A
Data Center – East
Secondary – B Secondary – CSecondary - A

SPEAKER NAME
Speaker title, company
and handle

SPEAKER NAME
Speaker title, Company
Handle
SPEAKER NAME
Handle

SPEAKER NAME
Handle
SPEAKER NAME
Handle
SPEAKER NAME
Handle

SPEAKER NAME
Handle
SPEAKER NAME
Handle
SPEAKER NAME
Handle
SPEAKER NAME
Handle

Icon Library (PDF)
https://www.dropbox.com/sh/28kkukpfjjgxzhs/AACJZlTs4g7WX7L6WAfvV5Oea?dl=0

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