[MUSIC PLAYING]
SPEAKER: This is a data professional, a.k.a.
data analyst slash engineer slash scientist.
We'll refer to them as the data pro.
They've recently joined a new company, and part of their role
requires them to turn their team's manufacturing plant
dashboard from a reactive one into a proactive one
so that their team can be aware of issues before they happen.
The data pro is here to help, and Duet AI
is here to help the data pro.
The first step is taking a look at the manufacturing plant
dashboard powered by Looker.
All of the plant's real-time structured and unstructured
data is loaded into Google Cloud,
analyzed, and presented here.
They can see that one of the plants is having some issues,
so they click into it to explore.
Immediately, they're able to see that throughput
has been below target for a number of hours
starting at 2:00 PM.
The data pro decides to use Duet AI and Looker to learn
why throughput is down.
In the chat window, they prompt Duet AI,
"Why did the throughput drop at 2:00 PM?"
Duet AI responds with the production line
that the throughput dropped for, and since it
knows the context of the situation,
Duet AI has gone one step further
and suggested additional questions
to ask, like this one, "Which metrics
correlate with the dip?"
From the response, the data pro can
see that there are some machine issues.
Duet has given some machine statistics
that are pro will use to start diagnosing the physical fix.
This is really helpful for our data pro.
Without Duet, they probably would
have had to ask another data engineer for help
since this data is not easily accessible.
Now our data pro can see why they've
been tasked with making this dashboard proactive instead
of reactive.
They need to build a preventative maintenance
model to identify future downtime and avoid it.
In order to build and ship a data model,
they'll get started in BigQuery Studio,
a simple unified environment where they can build anything
they want, including in Python.
First, they'll take a look at what data already
exists so they don't have to start from scratch.
There are two data sets, one in Google Cloud
and another in a different cloud.
They see a throughput table that looks
promising as a starting point.
They open it to see what the data is
and validate the quality of the data.
Once they open the table, they use the data profile tool
to perform some light exploratory data analysis.
They also see that there are automated quality checks
in place, which is a good sign.
So they know this data is actively maintained.
Oh, cool.
There is even an Insights tab where Duet AI has already
analyzed the data and how it's being used
and proactively suggests insights
for business questions that our data pro might want to answer.
They decide to open and run one of the queries in BigQuery
to further explore the insights.
They appreciate how helpful these insights
are to build their understanding of this data quickly.
It looks like another data set also has some reference data,
so they'll pull that into the model, too.
Since they're building multiple ML projects,
they decide to do this in a notebook inside of BQ,
so collaboration is easier.
Duet AI supports SQL generation and completion.
So they ask it to help build a model from the multi-cloud data
that can predict throughput based on the attributes
available.
In the notebook itself, they prompt Duet
by clicking plus code and typing in the prompt to create
a logistics regression model.
Duet AI will even understand the request
with incorrect spelling.
And just like that, the SQL is ready.
That saved our data pro a lot of time.
It also interconnected the data across clouds
using a simple SQL join in real time,
without the need for complex operations or processes here.
Duet AI helps our data pro not just
code faster but also in any language, in any analysis tool
to help them answer business questions.
Amazing.
Our data pro wants to make sure the model will actually
run on real-time data since it was trained on a table, not
a data stream.
In the same notebook, they can start mixing languages,
in this case using Python.
First, they use a few lines to see the model actively running
against the production data and with predictions.
The model is ready.
Predictions are a good start, but the data pro
wants to go further and actually help the plant operators
by giving them recommendations for how they can keep
the plant working optimally.
Luckily, there is some more data available,
this time unstructured inspector reports
that they can use for this.
They use retrieval augmented generation,
which is a fancy way of saying that they will
use their own data and an LLM to build an agent with tools
to connect to their data.
These tools will help improve the reliability of responses
from the LLM and reduce hallucinations.
Sounds like a lot of work, but they're
going to do it in a few lines of code.
First, they'll bring AI to the data
in BigQuery using LLMs powered by Vertex AI and BigQuery's
new ML inference engine to connect
all these amazing models, summarize the reports,
and create vectors, or numeric representations of their text,
to help with searches and groupings.
Next, they pass a question or prompt into the LLM
to get the vectors and summary.
Then they use the summaries to ask the LLM for recommendations
to get predictive actions, and the results are displayed.
This would have taken our data pro a lot of time and effort
to do using traditional data science methods.
Here, they did it in a few functions and a couple minutes
using the power of generative AI and all in BigQuery
where their data remains secure.
Now the pipeline is deployed.
Let's see the pipeline results back
in the plant dashboard where the predictive results are now
displayed.
Our data pro can even set actions using Looker
so that the right people are notified to take action
on the new recommendations.
Our data pro has succeeded in helping the dashboard move
from reactive to proactive using the Data Cloud and generative
AI to make it all happen in minutes, securely,
and at scale.
And with all that time saved, they can now finally get around
to playing ukulele.
Thanks, Duet AI.
What will you build?
[MUSIC PLAYING]