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_URI
docker 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 aiplatform

endpoint = 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. 

Google Cloud results

• Helps enable instant document approval with optical character recognition by Vision API
• Processes 100,000 documents in 20 minutes with automated document processing product Recognic, powered by Vision API and Apigee
• Helps increase the number of applications processed by 30% within the same timeframe

The insurance landscape in India has seen significant changes in recent years with the adoption of new technology. As one of the major insurance providers in the country, ICICI Prudential Life Insurance has aimed to lead in this transformation journey. “There has been a data explosion across India over the past few years, together with a high mobile penetration rate. Today, about 60% of our customers approach us via mobile, for example, which was certainly not the case before,” says Alpesh Karnik, SVP, IT, at ICICI Life Insurance.

Consumer expectations have also evolved, with easier access to information and online services. “Consumers today are more informed on the importance of investing in insurance products, so there’s much more of a pull factor when it comes to sales, but they also want to be able to get these products quickly and easily,” adds Alpesh. To meet the demands of these consumers, ICICI Prudential Life Insurance realized it needed to make its processes even faster and more efficient. Looking to upgrade its infrastructure, the company turned to Google Cloud.

“The biggest benefit of using Recognic and Vision API is that it eliminates the initial waiting time, which can result in drop-offs. Now customers can know immediately whether their documents are sufficient, or if they need to revise or submit any others.”—Alpesh Karnik, SVP, IT, ICICI Prudential Life Insurance

Serving customers better by speeding up processes with Google Cloud

ICICI Prudential Life Insurance’s distributors were already using tablets to input customer data faster and more efficiently, but many of the company’s solutions still required a team at the back end to manually sift through documents for approval. This meant that customers needed to wait five or six hours, or sometimes until the next working day, to know if their documents were approved or needed revision.

That all changed after partnering with Google Cloud Premier Partner Searce to take advantage of its AI/ML powered automated document processing product Recognic, which is built on Google Cloud. Developed using the optical character recognition (OCR) capabilities of Cloud Vision, Recognic reads, understands, and validates documents at scale, enabling organizations that handle massive amounts of paperwork to digitize these documents and then accurately store and index them.

“Google Cloud has cut down the middle- and back-office work, leading to a 30% increase in the number of applications we can process in the same time span without the need for additional resources.”—Alpesh Karnik, SVP, IT, ICICI Prudential Life Insurance

“In the case of ICICI Prudential, the biggest benefit of using Recognic and Vision API is that it eliminates the initial waiting time, which can result in drop-offs. Now customers can know immediately whether their documents are sufficient, or if they need to revise or submit any others,” Alpesh adds.

Alpesh explains that if the details on the application form match the documents provided, the case doesn’t need to go to the underwriter for further checks and can go directly to policy issuance. “Google Cloud has cut down the middle- and back-office work, leading to a 30% increase in the number of applications we can process in the same time span without the need for additional resources.”

ICICI Prudential Life Insurance is also working with Searce to build deep learning models into Recognic so that it can overcome template barriers and input data from a variety of forms. This is particularly helpful for financial and medical documents underwriting because unlike a passport or driving license, financial documents have a higher structural complexity.

As customer data becomes more important in the work of ICICI Prudential Life Insurance, so does protecting it, and the company is taking every measure to safeguard the security and privacy of its customers’ information. “Details of customers’ contactability are automatically removed by Google Cloud after processing is complete. This step in the workflow gives us the confidence that data is not stored at any level of the optical character recognition process,” says Alpesh.

Partnering with the right teams for dedicated support

In achieving the best solution for its business goals, ICICI Prudential Life Insurance recognizes the importance of its decision to work with partners that truly understand the insurance business. “There are many intricacies involved in this business, and it’s clear that both Google Cloud and Searce really took the time to understand our underwriting processes before coming up with a solution,” says Alpesh. He adds that during the implementation process, all findings were well documented and queries were responded to quickly.

“We didn’t want to take any shortcuts deploying Recognic, but at the same time, we didn’t want to draw out the implementation process. The excellent support from both Google Cloud and Searce throughout the journey was reassuring for us as they were always thinking ahead.”

Future-proofing the organization through machine learning and AI

In the coming years, Alpesh foresees the insurance industry to be even more agile than it is today. “I doubt elaborate processes such as underwriting or operations checks will need to be done manually in the future. Everything will be done through machine learning and AI.” In light of this, ICICI Prudential Life Insurance is doing everything it can to prepare, as customers’ expectations are set to keep evolving. “We have to be prepared for the future, and I believe that with Google Cloud, we can do it.”

About ICICI Prudential Life Insurance

ICICI Prudential Life Insurance aims to lead the Indian insurance field through quality products and a hassle-free claim settlement experience. A customer-centric company, it offers long-term savings and protection plans to meet customers’ needs at every stage of life.Industries: Financial Services & InsuranceLocation: India

About Searce

Searce is a niche cloud consulting business with futuristic tech in its DNA, focused on “realizing the Next in the Now” for its clients. Specializing in cloud data engineering, AI/ML, and ad

Editor’s note: Prevision.io has built the first ever pay-as-you-go AI management platform that simplifies the machine learning project lifecycle while offering powerful analytics capabilities. Now available exclusively on Google Cloud Marketplace, users can experiment, build, deploy, and manage AI projects in the cloud in weeks—without having extensive data science knowledge.

Similar to the momentum that cloud technology has had in the business world, AI and machine learning are quickly becoming essential to the enterprise. 86% of companies now view AI as a “mainstream technology,” and corporate AI adoption rose 50% in 2021 from the year prior for initiatives such as service-operations optimization and product enhancement. But for companies that don’t fall into the Fortune 500, initiating AI projects can come with several challenges, ranging from one-size-fits-all subscription models to skills gaps and resource-heavy monitoring requirements.

My team of data scientists saw a real need for software that could democratize machine learning innovation by removing these common barriers. We knew that features like automation could make building and deploying AI much more doable for other data scientists, as well as citizen data scientists. So, we launched Prevision.io, a first-of-its-kind dedicated AI management platform built on Google Cloud and now available exclusively on Google Cloud Marketplace.

As a Google Cloud Partner Advantage Member, we knew the benefits of an elastic infrastructure, full integration with BigQuery, and access to a large library of complementary tools, such as Kubeflow on Google Cloud. As a result, it’s easier for our users to improve upon their robust AI projects.

Set up in minutes, deploy a machine learning model in weeks

Users can start building and deploying models in Prevision.io immediately after subscribing through Google Cloud Marketplace. Instead of taking weeks to onboard and months to launch a real-world model into production, Prevision.io’s intuitive interface and powerful predictive analytics capability makes it possible to set up in minutes–and have models up and running in three to four weeks. Since we believe in cost-efficient scaling, our platform operates on a pay-as-you-go model with no long-term contracts, licensing, or per-user fees.

Simply connect Prevision.io with your data–whether it exists in buckets or in an SQL data source like BigQuery. No matter your data source, set up is quick. There are even tutorials to help if you’re using APIs. Follow this tutorial for step-by-step set up details in Google Cloud.

Once historical data is imported, you can start applying your own models inside Prevision.io, or use Prevision.io to build a bespoke model. There’s more good news: expenditures on Prevision.io’s platform are applied toward customers’ Google Cloud spend commitments.

Full lifecycle AI project management made easy

By automating the complexity of AI project management with a no-code approach, businesses do not have to add more data scientists to their teams, risk data drift or outdated models, spend hundreds of thousands on unused software, or massively expand IT budgets. By connecting BigQuery or other datastores to Prevision.io, you can launch high-performing machine learning projects and manage them across the entire lifecycle. With Prevision.io you can:

• Experiment with iteration and optimization to get an effective model into production from the start. Track performance and compare versions to identify the most reliable model. Because there is no infrastructure to manage, users can focus only on the project and see ROI sooner.
• Automate training and prediction tasks to improve collaboration, reduce time-consuming manual operations, and boost results. Users can implement automation across the production pipeline with built-in features like AutoML and a scheduler for recurring tasks. Retrain automations and integrate custom code to keep processes aligned and relevant.
• Deploy scalable working models securely and reliably in one-click. Tailor deployments using REST APIs or as a component to generate batch predictions. Create dashboards to share with stakeholders, and swiftly update your model without worrying about service interruptions or breakages.
• Monitor infrastructure and model behavior to understand resource utilization and how data changes over time—without requiring more of IT. Put an end to endless maintenance meetings with reliable, real-time monitoring applications around drift, data in-and-out, and prediction distribution. If an issue arises, Prevision.io provides detailed alerts and analysis to understand the root of a problem.

Any business can benefit

Regardless of industry or department, we’ve seen Prevision.io help businesses solve some of their biggest challenges. Utilities companies are relying on better forecasts of the energy consumption (gas or electricity).. Transportation companies have deployed machine learning models that can inform logistical operations based on fluctuating supply and demand. Doing more with data not only improves what a business can offer their customers but can also yield significant savings. Here are a few real-life examples:

La Poste: delivery data saves the day
Global delivery company La Poste was having trouble meeting customers’ demand for speed and visibility, and inaccurate estimated arrival times for packages was costing them money. The team wanted to put its tracking and tracing data to work, and turned to Prevision.io to select technical metrics, set up personalized machine learning models for delivery rounds of all personnel, and speed up the iteration and training process. After deploying its model in four weeks, La Poste achieved an 89% accuracy rate for delivery times and saw a 10x improvement in IT infrastructure and operational costs. And of course, happier customers who keep coming back.

BPCE: machine learning helps us help our clients
An arm of BPCE Group, the second largest banking group in France, was feeling the effects of the pandemic’s impact on customers. It needed a more efficient way of determining who would need what type of help—and when–to reduce the number of customers entering the collections process. Using its wealth of data to create and deploy a machine learning model in Prevision.io, the firm was able to rapidly identify the most at-risk customers and better understand the root causes behind potential debt default—some of which are easily fixable. As a result, the firm has seen a fifteen-fold increase in the sums they have been able to recover, and decreased the number of collection cases by 50%.

Pharmaceutical company: marketing medicine with MLOps
The marketing department at a healthcare company serving pharmacies was able to reduce customer churn and improve growth by using Prevision.io to compute market segmentation based on anticipated customer revenue. This helped the company determine the best way to engage with each pharmacy—and when. Being able to make strategic decisions based on automated predictions and more targeted data saved the company $1.3M Euros in two fiscal quarters.

See how AI project management and MLOps made easy can transform your business. Access Prevision.io on Google Cloud Marketplace and take advantage of the 14-day free trial.