Learn to Deploy Custom Models on Vertex AI
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In May we announced Vertex AI, our new unified AI platform which provides options for everything from using pre-trained models to building your models with a variety of frameworks. In this post we’ll do a deep dive on training and deploying a custom model on Vertex AI. There are many different tools provided in Vertex AI, as you can see in the diagram below. In this scenario we’ll be using the products highlighted in green:
AutoML is a great choice if you don’t want to write your model code yourself, but many organizations have scenarios that require building custom models with open-source ML frameworks like TensorFlow, XGBoost, or PyTorch. In this example, we’ll build a custom TensorFlow model (built upon this tutorial) that predicts the fuel efficiency of a vehicle, using the Auto MPG dataset from Kaggle.
If you’d prefer to dive right in, check out the codelab or watch the two minute video below for a quick overview of our demo scenario: https://www.youtube.com/embed/bHoAXR26hWo?enablejsapi=1&
Environment setup
There are many options for setting up an environment to run these training and prediction steps. In the lab linked above, we use the IDE in Cloud Shell to build our model training application, and we pass our training code to Vertex AI as a Docker container. You can use whichever IDE you’re most comfortable working with, and if you’d prefer not to containerize your training code, you can create a Python package that runs on one of Vertex AI’s supported pre-built containers.
If you would like to use Pandas or another data science library to do exploratory data analysis, you can use the hosted Jupyter notebooks in Vertex AI as your IDE. For example, here we wanted to inspect the correlation between fuel efficiency and one of our data attributes, cylinders. We used Pandas to plot this relationship directly in our notebook.
To get started, you’ll want to make sure you have a Google Cloud project with the relevant services enabled. You can enable all the products we’ll be using in one command using the gcloud SDK:
gcloud services enable compute.googleapis.com \containerregistry.googleapis.com \aiplatform.googleapis.com
Then create a Cloud Storage bucket to store our saved model assets. With that, you’re ready to start developing your model training code.
Containerizing training code
Here we’ll develop our training code as a Docker container, and deploy that container to Google Container Registry (GCR). To do that, create a directory with a Dockerfile at the root, along with a trainer subdirectory containing a train.py file. This is where you’ll write the bulk of your training code:
To train this model, we’ll build a deep neural network using the Keras Sequential Model API:
model = keras.Sequential([layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),layers.Dense(64, activation='relu'),layers.Dense(1)])
We won’t include the full model training code here, but you can find it in this step of the codelab. Once your training code is complete, you can build and test your container locally. The IMAGE_URI in the snippet below corresponds to the location where you’ll deploy your container image in GCR. Replace $GOOGLE_CLOUD_PROJECT below with the name of your Cloud project:
IMAGE_URI="gcr.io/$GOOGLE_CLOUD_PROJECT/mpg:v1"docker build ./ -t $IMAGE_URIdocker run $IMAGE_URI
All that’s left to do is push your container to GCR by running docker push $IMAGE_URI. In the GCR section of your console, you should see your newly deployed container:
Running the training job
Now you’re ready to train your model. You can select the container you created above in the models section of the platform. You can also specify key details like the training method, compute preferences (GPUs, RAM, etc.) and hyperparameter tuning if required.
Now you can hand over training your model and let Vertex do the heavy lifting for you.
Deploy to endpoint
Next, let’s get your new model incorporated into your app or service. Once your model is done training you will see an option to create a new endpoint. You can test out your endpoint in the console during your development process. Using the client libraries, you can easily create a reference to your endpoint and get a prediction with a single line of code:
from google.cloud import aiplatformendpoint = aiplatform.Endpoint(endpoint_name="projects/YOUR-PROJECT-NUMBER/locations/us-central1/endpoints/YOUR-ENDPOINT-ID"). . .endpoint.predict(test_instances)
Start building today
Ready to start using Vertex AI? We have you covered for all your use cases spanning from simply using pre-trained models to every step of the lifecycle of a custom model.
- Use Jupyter notebooks for a development experience that combines text, code and data
- Fewer lines of code required for custom modeling
- Use MLOps to manage your data with confidence and scale
Get started today by trying out this codelab yourself or watching this one hour workshop.
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An Introduction to MLOps on Google Cloud
The enterprise machine learning life cycle is expanding as firms increasingly look to automate their production ML systems.
MLOps is an ML engineering culture and practice that aims at unifying ML system development and ML system operation enabling shorter development cycles, increased deployment velocity, and more dependable releases in close alignment with business objectives.
In this video, Nate Keating, Product Manager, Google Cloud, will define and give an overview of MLOps and the discuss the challenges at play. He then shares where data science teams are today and where Google Cloud sees them going. Finally he will demonstrate a simple framework for MLOps based on real processes that he has seen in practice.
Learn how to construct your systems to standardize and manage the life cycle of machine learning in production with MLOps on Google Cloud.
Unified, Flexible and Accessible: How Companies’ Data Help Them Achieve More on Google Cloud
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As the volume of data that people and businesses produce continues to grow exponentially, it goes without saying that data-driven approaches are critical for tech companies and startups across all industries. But our conversations with customers, as well as numerous industry commentaries, reiterate that managing data and extracting value from it remains difficult, especially with scale.
Numerous factors underpin the challenges, including access to and storage of data, inconsistent tools, new and evolving data sources and formats, compliance concerns, and security considerations. To help you identify and solve these challenges, we’ve created a new whitepaper, “The future of data will be unified, flexible, and accessible,” which explores many of the most common reasons our customers tell us they’re choosing Google Cloud to get the most out of their data.
For example, you might need to combine data in legacy systems with new technologies. Does this mean moving all your data to the cloud? Should it be in one cloud or distributed across several? How do you extract real value from all of this data without creating more silos?
You might also be limited to analyzing your data in batch instead of processing it in real-time, adding complexity to your architecture and necessitating expensive maintenance to combat latency. Or you might be struggling with unstructured data, with no scalable way to analyze and manage it. Again, the factors are numerous—but many of them accrue to inadequate access to data, often exacerbated by silos, and insufficient ability to process and understand it.
The modern tech stack should be a streaming stack that scales with your data, provides real-time analytics, incorporates and understands different types of data, and lets you use AI/ML to predictively derive insights and operationalize processes. These requirements mean that to effectively leverage your data assets:
- Data should be unified across your entire company, even across suppliers, partners, and platforms., eliminating organizational and technology silos.
- Unstructured data should be unlocked and leveraged in your analytics strategy.
- The technology stack should be unified and flexible enough to support use cases ranging from analysis of offline data to real-time streaming and application of ML without maintaining multiple bespoke tech stacks.
- The technology stack should be accessible on-demand, with support for different platforms, programming languages, tools, and open standards compatible with your employees’ existing skill sets.
With these requirements met, you’ll be equipped to maximize your data, whether that means discerning and adapting to changing customer expectations or understanding and optimizing how your data engineers and data scientists spend their time. In coming weeks, we’ll explore aspects of the whitepaper in additional blog posts—but if you’re ready to dive in now, and to steer your tech company or startup towards success by making your data better work for you, click here to download your copy, free of charge.
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AI-powered Cameras Help You Serve Your Pets Better. Learn How
Watch the video to learn how you can leverage Google Cloud platform and AI functions to build pet-detection cameras that can capture and send image-based alert messages on your phone to notify when your pets arrive or leave.
Pindrop and Google Cloud Get Together to Make Way for Next Wave, Voice-first Interfaces!
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If you’ve ever used only your voice to authenticate a payment, place an order, or check an account over the phone, there is a good chance that Pindrop’s technology made it possible.
Founded in Atlanta in 2011, Pindrop provides software and technology that uses machine learning, voice recognition, and behavioral analytics to help detect and prevent fraud in voice channels like AI-powered call centers and solve the long standing identity problem commonly encountered in customer care and contact center experiences. Demand for Pindrop’s technology has grown considerably over the past decade, as more organizations and people adopt voice as an interface for customer service, transactions, entertainment and more. As that demand has grown, safeguarding these voice interactions from fraud or authentication loops continues to be a unique challenge.
Now, with frictionless voice-driven customer experiences at an all-time high, cybersecurity is more critical than ever. In fact, research shows that fraudsters are able to accurately answer knowledge-based authentication questions 92% of the time, as opposed to only 46% for actual customers. To address this, Pindrop is bringing its products and technology to Google Cloud in order to scale and develop new capabilities that will help enable the next wave of voice-first interfaces and call centers.
The new partnership will enable Pindrop’s teams to accelerate their development of new capabilities to address an ever-shifting landscape – like detecting deep fakes and robocalls, helping banks authenticate transactions, providing retailers with AI-powered call center support, and helping IoT makers optimize for security, speed and simplicity.
More specifically, the partnership will help Pindrop scale its own businesses, and meet ever-growing customer needs in several ways.
First, Pindrop is bringing its SaaS platform to Google Cloud, enabling customers to deploy it at global scale, on secure, sustainable, and highly performant cloud infrastructure. Not only does this give Pindrop the ability to deliver secure voice interactions with low-latency, it also aligns with its mission to enable customer choice when selecting and implementing security technology. And for customers already operating on Google Cloud, getting started with Pindrop can be done directly within their cloud environment, now that the company is making its solutions available on Google Cloud Marketplace.
Second, Pindrop plans to offer its voice secure technology with Google Cloud’s Contact Center AI (CCAI) capabilities to develop new solutions that will enhance call center experiences. CCAI capabilities in understanding, augmenting, and ML-processing conversations allows companies to deliver interactions that are helpful and keep costs low. These capabilities alongside Pindrop’s authentication and anti-fraud solutions will enable the company with the tools and technologies needed to create the next evolution of frictionless experiences in a secure way.
Finally, building on top of Google Cloud will equip Pindrop with multicloud capabilities that will offer new channels to increase revenues through. Using technologies like Google Kubernetes Engine, frequently chosen by startups and tech companies, to quickly launch and scale new ML applications up or down will also support Pindrop’s growth as it adds to its suite of technologies.
Supporting innovative startups and digital native businesses like Pindrop is in Google’s DNA, and we’re delighted to support Pindrops’ growth, and the success of their customers, with Google Cloud infrastructure, Contact Center AI, and go to market expertise.
You can learn more about Pindrop on Google Cloud here, and Google Cloud’s Contact Center AI solution here.
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Satellites Can Help Map Carbon Emissions By Looking at Images of Power Plants!
Did you know that Satellites can now help track power plants and determine if they are on or off? And did also know that compute processing that classifies over 59 trillion bytes of data from over 11,000 sensors from 300 satellites is done on on Google Cloud?
Google Cloud’s sustainability initiatives along with its cutting-edge, powerful technology and products are leveraged worldwide by organizations, non-profits and governments to keep a check on greenhouse gas emissions and build self-reporting, power monitoring platforms. Climate TRACE, a collaborative data sharing project with 50+ organizations is dedicated towards this cause by building a visual and meaningful way on a web app. In this video you can learn how generate geo-spatial model that can view the images of power plants gathered from satellite called Sentinel-2 to assess emissions from power plants. Watch the video to learn Google Cloud’s vehement role in funding and staffing special initiatives with Googlers who are experts in AI/ML, UX, data analytics and more!
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