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MATHIEU GUILLIAME-BERT: Hi, everyone.
I hope you are doing well.
In this talk, we are going to show you a new product
to use machine learning in Google Sheets without coding
and without knowing much about machine learning.
We have called this product Simple ML for Sheets,
where ML stands for machine learning.
I'll be giving this talk with Richard.
He's also a software engineer working on Simple ML,
and you will see him presenting the demo.
Here's a quick outline of the presentation.
A good example is worth a thousand words.
So we will start with two demo of Simple ML for Sheets.
You will first see how to predict
missing value in a spreadsheet.
While predicting missing values sounds simple,
it's very useful when value are unknown
or when they are available but expensive to obtain.
Then, in a second demo, you will see how to do forecasting.
Forecasting means predicting future value.
And it's very useful for decision making.
In the demo, we will use forecasting
to predict sales of a business.
And I'm sure you can see the value that forecasting
sales can bring.
Each demo will run in two clicks,
but a lot will be going on in the background.
This is the kind of power Simple ML for Sheets bring.
Finally, in the third part, we will
talk about how Simple ML works and how a technical user can
benefit from it.
And with that, I will hand it over to Richard for the demo.
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RICHARD STOTZ: Thank you, Mathieu.
To make our first demo more exciting,
I invite you to consider the following scenario.
Suppose you are a biologist who studies penguins in Antarctica.
During your last expedition, you visited
a number of penguin colonies.
You measured the size, weight, and other data
of many penguins.
In total, you recorded data for 300 penguins.
Unfortunately, you were distracted
by the beauty of Antarctica, and you
forgot to record the species--
chinstrap, gentoo, or Adelie--
for around 30 of the penguins.
Now you have a spreadsheet with all your penguin data,
but 30 values are missing.
Can we use Simple ML to recover those missing values?
Let's find out.
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This is a spreadsheet containing all the information you
collected about the penguins.
Each row represents a single penguin
and shows its attributes.
The last column, called Species, contains
the penguin's species--
Adelie, gentoo, or chinstrap.
You can see that some species values are missing.
Those are the values you forgot to record.
Let's use Simple ML to recover them.
First, start the add-on in the Extension menu.
After a few seconds, the Simple ML sidebar appears.
And this sidebar works as follows.
You start by choosing a task, describing what you want to do.
Next, you configure this task.
And finally, you run the task.
So back to our penguins, we want to recover the missing
values of the Species column.
So we select the Predict Missing Values task.
Then we configure the task.
Here we want to make sure that the Species column is indeed
selected.
Finally, we press Predict.
And that's it.
The task is now running.
This may take a couple of seconds.
And it's done.
Simple ML for Sheets has computed the missing values.
For each missing value, we can see the predicted value here.
For example, this animal is a gentoo.
And the model is pretty certain about this,
over 99% confidence.
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Let's summarize what we did in this first demo.
First, we reached our goal.
We successfully predicted the missing values in our sheet.
Now we know the species of all the penguins.
In addition, we also know how confident
we can be in the predictions made by Simple ML for Sheets.
This is important because machine learning can sometimes
make mistakes.
And it is important to understand
when it is likely to do so.
And, finally, and probably the most exciting part,
we did this without writing a single line of code.
In fact, we mostly had to click on a single blue button--
not too bad.
Next, we are excited to share the latest feature of Simple
ML for Sheets, forecasting.
Forecasting means predicting future values from past ones.
This is a challenging task that has fascinated people
for a long time because knowing what will happen in the future
is key for decision making.
Let's see how we can use Simple ML for Sheets
to do forecasting.
Once again, I invite you to consider a real-world scenario.
Imagine you are a manager of a fantastic Swiss restaurant.
You know that your restaurant could reduce food waste
if you knew ahead of time which menus
your customers want to order over the next four weeks.
However, just following your instincts
has proven difficult for this task.
You might also want to plan how many staff members
you need to hire and many other business decisions.
With forecasting, you can use the information
about past sales to anticipate future sales.
This enables you to plan better and make more sustainable
decisions for your business.
Let's see how this works using Simple ML for Sheets.
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Let's go back to Google Sheets.
This spreadsheet contains all the information
you collected about the restaurant's sales data.
You see that the restaurant has data for five menu items--
sparkling water, lemonade, raclette, fondue,
and chocolate.
In addition, items are categorized as drinks,
main dish, or dessert.
As you know, Google Sheets is an excellent tool
for data visualization.
So we already created a plot of the past sales of those items.
If we scroll down, we see the future dates
for which we want to forecast.
Let's use Simple ML to generate this forecast.
You can see that we already started Simple ML for Sheets.
So choose the Forecast Future Values
task from the task selection.
Simple ML for Sheets has already selected all menu items
for forecasting, and this is what we want.
We press Forecast to run the forecasting algorithms,
and that's it.
The task is now running.
This may take a couple of seconds, and it's done.
We see the future sales in the sheet.
Let's add these forecasted values
to the chart to see if they are reasonable.
Just double-click on the chart and then on Add Series.
Pick Pred:Sparkling Water to see the predictions for sparkling
water in our chart.
Those look pretty reasonable.
The forecasting algorithm is clearly
able to identify the seasonal patterns in our data.
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Let's summarize the benefits of forecasting with Simple
ML for Sheets in this use case.
You know now the likely sales over the next four weeks.
You did not write a single line of code.
You can start to forecast other aspects of your business
in a similar way.
There are many exciting possibilities.
For instance, you can forecast a number
of clients over the next week or forecast the maintenance
cost of your equipment.
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MATHIEU GUILLIAME-BERT: So you've
just seen how to do machine learning
without doing any machine work, and I'm sure you like it.
However, if you are already familiar with ML,
you might want more control of what is happening.
For example, you might want to train model and configure
hyperparameter.
You might want to evaluate, interpret, or even combine
model.
The great news is Simple ML allows you to do exactly that.
Simple ML contains advanced tasks.
For example, you can train a model,
evaluate a model, and so on--
simple and powerful.
And if you are really serious, you
can even export model to, let's say, TensorFlow
and use them in a Colab.
In conclusion, I hope those example
pique your curiosity to install Simple ML for Sheets
and start experimenting with it.
As you saw, there is no coding involved,
and you do not need to know machine learning to get result.
In addition, all operations such as the model training
are done exclusively in your browser
on your computer, which means that your data stay in Google
Sheets and on your computer.
In addition, for advanced ML user
or for those who are learning about machine learning,
Simple ML gives you control.
You have seen in the demo that operation
were almost instantaneous.
This was real time.
Simple ML is fast, generally running just
in second, which is fantastic for fast iteration.
And it gives you great results.
In fact, Simple ML uses some of the same learning algorithm
that TensorFlow uses.
Finally, Simple ML is compatible with a rich ecosystem
of tools and library.
For example, you can export your model
to TensorFlow and TensorFlow GS.
But you can also run them directly in C++ or even in Go
using Yggdrasil decision forests.
And this is the end of this talk.
Please visit our website to try Simple ML for Sheets.
If you only have five minutes, I recommend that you
start with the first tutorial.
If you have questions, please ask them in the user group.
And, of course, feedback are always welcome.
And for advanced user, I also put
the link of Yggdrasil decision forests in TensorFlow.
On this note, I want to thank you for your time,
and we hope you will find Simple ML for Sheets
useful in your works and projects.
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