MongoDB.local DC 2018: Building Intelligent Apps with MongoDB & Google Cloud
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The document discusses the development of intelligent applications using MongoDB and Google Cloud to enhance e-commerce experiences through real-time analytics and machine learning. It highlights the challenges of implementing AI in business and provides insights into building an intelligent e-commerce app, Swagstore, which employs recommendation engines and chatbots for personalized shopping experiences. Additionally, it outlines the integration of various services and tools to streamline app development and optimize machine learning models.
![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"
]}](https://image.slidesharecdn.com/1025-room3-buildingintelligentappswithmongodbandgooglecloud-mongodb-181009165826/85/MongoDB-local-DC-2018-Building-Intelligent-Apps-with-MongoDB-Google-Cloud-21-320.jpg)
![https://jfmlrecengine.appspot.com/recommendation?
userId=5448543647176335931&numRecs=6
{"articles":
["299824032"
,"299935287"
,"299865757"
,"299959410"
,"298157062"
,"299816215"
]}](https://image.slidesharecdn.com/1025-room3-buildingintelligentappswithmongodbandgooglecloud-mongodb-181009165826/85/MongoDB-local-DC-2018-Building-Intelligent-Apps-with-MongoDB-Google-Cloud-22-320.jpg)
MongoDB.local DC 2018: Building Intelligent Apps with MongoDB & Google Cloud
- 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
- 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
- 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 using TensorFlow WALS Algorithm Google Cloud Endpoints serve 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
- 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?user Id=5448543647176335931&numRecs=6 {"articles": ["299824032" ,"299935287" ,"299865757" ,"299959410" ,"298157062" ,"299816215" ]}
- 23. 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.
- 26. Intelligent eCommerce App: SwagStore + Shop for products via chat interface AI Chatbot
- 27. Intelligent SwagStore: How We Built It SwagStore Google DialogFlow: Intent, Entities, Webhooks Stitch - Intent Fulfilment Slack - Front End developer data scientist developer data engineer
- 28. DialogFlow + Stitch Architecture Stitch HTTP Service Webhook Stitch Functions to retrieve products from MongoDB
- 29. 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
- 30. Enabling DialogFlow Fulfillment With Stitch Stitch security enabled by default
- 31. 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
- 33. Working Together Developers Data Scientists DATA PREP BUILD MODEL GET INSIGHT VIZ DEPLOY TRAIN/EVAL RELEASE DEFINE BUILD AGILE
- 34. Build. Plug In. Innovate. Intelligent App
- 35. 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
- 36. Thank You!