Hey everyone, I'm Jeff Hollan, Product Manager here at Snowflake,
and I am so excited to show you Snowpark Container Services,
the Snowpark Model Registry, and NVIDIA Accelerated Infrastructure all in action.
So today, we're going to be training a model to help prevent and predict machine failures
on our manufacturing line. Now, to do this we're going to train based on data that has
been streamed into Snowflake. Now, when I build my machine models I love to use Notebooks, and
Snowflake partners like Hex provide some amazing Notebook experiences. But here, I'm going to be
using our proprietary data and to normally use this type of data with a third-party application like Hex,
I generally have to go through a bunch of different approvals - but not anymore.
Using the Snowflake Native App Framework, now in public preview, I can install third-party applications
in my Snowflake account. And with Snowpark Container Services, now in private preview,
I can run any container in my account including third-party applications.
So for this demo,
I've already installed the Hex app from the Snowflake Marketplace. You can actually see it now
running powered by Snowpark Container Services. Look at the app's URL - you can tell this is
no longer hex.tech. We can even ask Hex, using the LLM-powered Hex magic, where it is running.
And you'll notice back the friendly response: yes, we're running right in Snowpark Container Services.
Okay, so now let's get the data ready for training. We're going to use Snowpark Python DataFrames
to define a new set of rolling window aggregations so I can get the average temperature
from these machines over the last week, month, and year. Now once I have that data, I'm going
to combine these features along with the historic machine failures. And now I'm going to have my
table ready for model training. Now with Snowpark Container Services I can accelerate the training
of this XGBoost model using the integrated NVIDIA AI Platform, which includes NVIDIA GPUs and secure
end-to-end software as part of NVIDIA AI Enterprise. So for the software image, I've
already selected the RAPIDS data science libraries included in NVIDIA AI Enterprise. And for the
instance you can see here I've selected Snowpark Container Services with GPUs. So in the interest
of time, I've already completed the training for a 50 million record data set and it only took 17
minutes with the power of the NVIDIA AI Platform. Similar tests took over 10 times longer without
NVIDIA acceleration, and that massive speed boost results in a 2X cost savings. It's that simple.
Minimal code changes with immediate speed ups that have increased my developer productivity.
Now before productionizing this model, I want to make sure it's governed, discoverable, and observable
so that any team in my organization can find it and use it securely with their data. With the
Snowpark Model Registry, now in private preview, I can do this all in a scalable way. So I'm going to
run this code so that the model is now registered and it can easily be deployed to run inference
using the same NVIDIA AI Platform and quickly help other teams in my organization identify
machine downtime periods that could be prevented.
Okay, so let's recap some of the amazing tech that we just saw.
With Snowpark Container Services, I can now run containers including
third-party applications like Hex entirely in my Snowflake account. We use Snowpark DataFrames to
get the data ready for training, and with NVIDIA Accelerated Infrastructure, I was able to speed
up the training of an XGBoost model 10 times. Now finally, we took that model and registered it into
Snowpark's Model Registry.
If you want to learn anything more about these exciting announcements,
be sure to check out the link in the description. And please subscribe to this channel to stay up to date
with the latest news and announcements at Snowflake.
Thank you so much.