Even if you have terabytes of business data, it might not be easy to apply AI-based analytics to it. The bottleneck is often Machine Learning (ML) expertise and scalable infrastructure. We'll first look at how you can access vast amounts of data from the data warehouse directly in a Google Sheet. Then, you'll see how it's possible to train custom ML models with that data, without ever leaving the spreadsheet. Speaker: Karl Weinmeister Google Cloud AI Advocacy Manager

1. AI-Based Analytics in the Cloud
Karl Weinmeister {Developer Advocacy Manager}
@kweinmeister

2. 2020 State of the CIO report
Data science is one of the most difficult roles to fill
2020 RELX Emerging Tech Executive Report
A leading reason for companies not using AI is
lack of technical expertise
AI skills shortage still persists

3. Spreadsheets are everywhere!
https://twitter.com/jsoltero/status/1238496597064863744

4. How does data makes its way into spreadsheets?
Report
Query
API
Extract

5. 1. Data freshness
2. Data size
3. Anything else?
What are some issues with this approach?

6. Authorization to the data - what can happen?
Source Table Spreadsheet
A B C
Authorized users:
A D
Users:
Emailed to
Extracted to

7. Combining the best of Big Query and
the familiarity of Sheets to empower
workforces and assist with:
Introducing Connected Sheets
Sheets
Easy to use &
shareable
Familiar interface
Light-weight analysis
BigQuery
Analyze petabytes
of data
Complex queries
Increase time to insight
Connected
Sheets
Analyze billions of
rows of data in
Sheets, without any
need for specialized
knowledge.
Introducing
● Unlocking big data insights
● Accelerating data workflows
● Improving cost-efficiency
● Strengthening data security

8. Google BigQuery
Petabyte-scale storage
and queries
Encrypted, durable and
highly available
Real-time analytics on
streaming data
Google Cloud Platform’s
enterprise data warehouse
for analytics
Convenience of
standard SQL
Fully managed and serverless

9. Google BigQuery

10. Train and deploy ML models in
SQL
BigQuery ML
Execute ML workflows without
moving data from BigQuery
Automate common ML tasks
Built-in infrastructure
management, security &
compliance

11. Supported models in BigQuery ML
Classification
Logistic regression
XGBoost
DNN classifier (TensorFlow)
Regression
Other Models
k-means clustering
Time series forecasting
Model
Import/Export
Importing TensorFlow
models for batch prediction
NDA
AutoML Tables
Linear regression
XGBoost
DNN regressor (TensorFlow)
AutoML Tables
Recommendation:
Matrix factorization
Exporting models from
BigQuery ML for online
prediction

12. Demo!

13. Case Study: Demand Forecasting

14. https://unsplash.com/photos/9qQTUYm4ss4 https://unsplash.com/photos/M5tzZtFCOfs
Transportation Telecommunications
https://unsplash.com/photos/4P0zdOSstqI
Media/Gaming
➔ Predict ticket sales ➔ Predict network traffic
➔ Predict # active players/time
➔ Predict content viewership

15. https://unsplash.com/photos/nHRXNv2qeDE
Retail

16. Actual demand
Supply
Underallocation = revenue loss
Overallocation = revenue waste
Time

17. Iowa Liquor Sales data
Transactional data:
Iowa Liquor Sales data, BigQuery Public Datasets
https://console.cloud.google.com/marketplace/details/iowa-department-of-commerce/iowa-liquor-sales
`bigquery-public-data.iowa_liquor_sales.sales`

18. Training data SELECT
date,
item_description AS item_name,
SUM(bottles_sold) AS total_amount_sold
FROM
`bigquery-public-data.iowa_liquor_sales.sales`
GROUP BY
date, item_name
HAVING
date BETWEEN DATE('2016-01-01') AND
DATE('2017-06-01')

20. Developer Days
CREATE OR REPLACE MODEL
iowaliquor.forecast_by_product
OPTIONS(
MODEL_TYPE='ARIMA',
TIME_SERIES_TIMESTAMP_COL='date',
TIME_SERIES_DATA_COL='total_amount_sold',
TIME_SERIES_ID_COL='item_name',
HOLIDAY_REGION='US'
) AS
SELECT
date,
item_name,
total_amount_sold
FROM
iowaliquor.training_data
Build and train with
CREATE MODEL
https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series
Behind-the-scenes
● Pre-processing
● Holiday effects
● Seasonal and trend
decomposition
● Trend modeling with
ARIMA and auto-ARIMA

21. Developer Days
SELECT
*
FROM
ML.FORECAST(MODEL iowaliquor.forecast_by_product,
STRUCT(30 AS horizon,
0.90 AS confidence_level)
)
Making forecasts with ML.FORECAST

22. More info on the demand forecasting use case
Google Cloud Blog YouTube

23. Building your own AI solution

24. Solution Requirements
Consider a typical Enterprise use case where custom Machine Learning
Models need to built and shared with people throughout your org
Requirements:
● Build ML custom models quickly
● Easily expose ML Models to people throughout the org
● Allow users with little or no ML experience to run model analysis
● Iterate and adapt quickly
Solution technologies:
● Sheets - Familiar easy data access for entire org
● BigQuery - Enterprise data storage and quick analysis
● BigQuery ML - Streamlined ML model creation on BigQuery data using SQL
● Connected Sheets - Access BigQuery data directly from Sheets
● Apps Script - Connect Sheets/Workspace to ML Models

25. Confidential + Proprietary
Apps Script
➔ Scripting language based on JavaScript
➔ Automates tasks across Google products and
services
➔ Serverless - code editor in your browser, and
scripts run on Google’s servers
script.google.com

26. Data Analyst Workflow
Sheets Apps Script BigQuery ML

27. Data Scientist Workflows
R-BigQuery Integration
(bigrquery package)
RStudio
R AI Platform Notebooks on GCP
pandas-BigQuery Integration
Colab, Other Jupyter
Notebook Tools
Python AI Platform Notebooks on GCP
Python R
BigQuery

28. Demo!

29. More info on building your own ML solution in Sheets
Google Cloud Blog Code Sample Demo Spreadsheet
bit.ly/ml-sheet
Access all assets from blog post at:

30. Questions?