Peter-John Welcome GDE Firebase @pjapplez Building models from structured data with AutoML Tables An Introduction to AutoML Tables 

Half screen photo slide if text is necessary Who I am Mobile Engineering Lead at DVT Google Developer Expert for Firebase 

Structured vs Unstructured Data 

● Quantitative Data ● Consistent field types ● Easily searchable ● Similar to relational databases or spreadsheets ● Eg. Dates, Address, Coordinates, Money Structured Data 

● Qualitative Data ● Inconsistent ● More difficult to analysis and process ● NoSQL Databases ● Eg. Text, Audio, Images, Video Unstructured Data 

Structured and Unstructured data https://learn.g2.com/structured-vs-unstructured-data 

AutoML Tables 

ML Decision Pyramid https://www.youtube.com/watch?v=pm_-pVPvZ-4 

AutoML Tables Define your problem Retails Customer History: I am a retail company with customers and would like to predict how much money a customer will spend and what kind of products they are interested in. Conference Abstracts: I have a bunch of conference abstracts and I would like to predict if my abstract is likely to be accepted or reject. 

AutoML Tables Define your problem 

AutoML Tables https://cloud.google.com/automl 

● Allows us to build and deploy ML models at speed and scale ● Supports 3 types of Models: Binary classification, Multi-class classification, Regression Models ● Has the ability to generate a REST API to access the model ● Gives you the ability to Extract the model to test locally AutoML Tables 

● Training Data is split into 3 Categories: Training, Validation, Testing ● By default its a 80(Training), 10(Validation),10 spilt(Testing) - This can be modified ● Must have a minimum of 1000 rows ● There is the ability to choose your optimization objectives AutoML Tables (Train) 

● Precision and Recall graphs ● Confusion Matrix ● Interactive Threshold score for precision and recall accuracy ● Feature Importance AutoML Tables (Eval.) 

● Online Prediction ● Batch predictions through BigQuery ● REST endpoint deployment for client side interaction ● Extraction of model to docker container to deploy in any environment AutoML Tables (Predict) 

Demo Time 

● Easy to setup and get a model trained ● Easy to deploy and lets Google Cloud scale the model for you ● Model Size was really large ● No way to extract the Model to TF model or TFLite model ● Can get very expensive to Train large sets of data. ● Can get expensive to deploy model. AutoML Tables (Observations) 

AutoML Tables (Ref.) ● https://cloud.google.com/automl-tables/docs ● https://www.youtube.com/watch?v=pm_-pVPvZ-4 ● https://www.youtube.com/watch?v=tWbiOuHae0c ● https://www.youtube.com/watch?v=MqO_L9nIOWM ● https://www.youtube.com/watch?v=XrMtF_inTZ0 

Peter-John Welcome GDE Firebase @pjapplez Thank You!