PRIYANKA: Hi, I'm Priyanka [INAUDIBLE],,
and this is AI Simplified, where we
learn to make our data useful.
In this video, we're going to imagine a company that
has large amount of data which they want
to use to make meaningful predictions,
and take their business to the next level.
And we also know that their team has varying levels
of machine learning expertise.
To accelerate this AI innovation,
they are looking at Vertex AI, which
provides tools for every step of the machine learning workflow
from managing data sets to different ways of training
the model, evaluating, deploying,
and making predictions.
In this episode, let's see the first step
of the machine learning lifecycle,
which is the data sets.
You don't need to worry about data sets.
If your use case is generic enough,
you can just take advantage of the pre-trained machine
learning APIs.
And to learn more about them, I've included a link below.
In order to create custom machine learning models,
we must first have a collection of data
to train our models with.
That is what data sets are for.
Data sets make data discoverable from a central place.
They provide the ability to annotate and label the data
right from within the UI.
You can track the lineage for data governance
and can compare model metrics between the models.
Now, to create a data set, we first select our data
and upload it, import it, into the managed data sets.
And then we can make modifications and use
it to start training the model.
Currently, there are four supported data types--
image, tabular, text, and videos.
Now, image data sets currently support image classification,
which is where models predict one or many labels
from an image.
Now, for example, identifying the types of dog
treats from images of treats.
Object detection is where the model draws bounding boxes
around the items of the image.
For example, you're identifying the location
of different vegetables within a picture of salad.
Image segmentation is where the model
can assign a label to pixel level regions in an image.
To ensure your model performs well in production
and there's no training serving skew,
make sure the training images are
close to the input your users will
send to your deployed model.
For example, if your images are going to be low resolution,
be sure to have blurry images, low resolution images,
in there.
Include multiple angles, backgrounds,
and all sorts of resolutions.
It's recommended that you include at least 1,000 images
per label.
But, you can always get started with 10 per label.
The more examples you provide, the better your model will be.
Now, tabular data sets currently support regression,
where you predict a numerical value, and classification,
where you predict a category associated
with a particular example.
Forecasting is where the model predicts likelihood
of sudden events or demands.
For example, you want to predict sales over a period of time.
Now, tabular data sets support hundreds
of columns and millions of rows.
Text data sets currently support classification,
which is where you assign one or more labels
to an entire document.
In a customer support example, an email
could include classifications as great service or a suggestion.
Now, entity extraction is where the model
identifies custom text entities within a document.
For example, maybe you want to identify all the phrases
in a customer support email that reference pricing.
Too expensive or great value.
Sentiment analysis is to understand
the overall sentiment expressed in a block of text.
For example, if a customer was happy or upset or frustrated.
Now, will your data sets currently
support classification, which provides labeled predictions
for an entire video, shots, and frames?
For example, in a football game, you
would be able to label the section with commercials
and the section with the game.
Now, action recognition is where the model
identifies clips in the video where specific actions occur.
For example, in a puppy football game, the action of a dog
making a touchdown.
Now, object tracking is where you get labels, bounding boxes,
and time stamps for objects you want to track in a video.
For example, in the same game, you
want to track the football's motion throughout the video.
Now, we just covered the basic functionality here.
I have included the link below for more details and best
practices on data preparation.
At this point, I think we want to see how to create and manage
the data sets in the console.
So let's dive in.
In the console, we get to Vertex AI and click
on the data set where we see image, tabular, text,
and video data sets for the different objectives we talked
about earlier.
Let's try to create an image data set.
Say we want to do image classification.
Before we begin, it's good to check
the Data Guide that shows us the requirements
and recommendations.
You can import files directly from your computer,
and they would be stored in Cloud Storage.
We can then add the corresponding labels
for our images in the next step.
If you already have labels, use the Import File option
to import a CSV with your image URLs and their labels.
The entries in the CSV would look something like this.
If your data is not labeled, and you
would like human help to label it,
then you could use the data labeling service.
Let's upload a few files here.
Once the files are uploaded, we can create labels
and assign them to the images.
We can also analyze the images we
have in the data set, number of images per label
and a few other properties.
Depending on the type of data, your options
might vary slightly.
For example, if you look at tabular data,
you could upload a CSV file from your computer,
from Cloud Storage, or select a table from BigQuery directly.
Once you select the table, the data is available for analysis.
We can see the number of rows, columns, et cetera.
You would do the same for a text data set.
Once uploaded, we can see the labels
or add new labels to the data.
And we can also analyze a data set.
The video data set interface looks similar.
I have some labeled sports files here for golf, cartwheel,
horse riding, and others.
That was a high level overview of how
you can manage different data sets in Vertex AI.
In the next video, we will look at the next steps of machine
learning workflow to build and train ML models.
In the meantime, let's continue our discussion
in the comments below.
I am excited to hear all about your ML use case
and your data sets.