At Google I/O this year, we introduced Vertex AI to bring together all our ML offerings into a single environment that lets you build and manage the lifecycle of ML projects. In a previous post, we gave you an overview of Vertex AI, sharing how it supports your entire ML workflow—from data management all the way to predictions. Today, we’ll talk a little about how to manage ML datasets with Vertex AI.

Many enterprises want to use data to make meaningful predictions that can bolster their business or help them venture into new markets. This often requires using custom machine learning models—something not every business knows how to create or use. This is where Vertex AI can help. Vertex AI 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. It also supports varying levels of ML expertise, so you don’t need to be an ML expert to use Vertex AI.https://www.youtube.com/embed/CN2X6oIlnmI?enablejsapi=1&

Types of data you can use in Vertex AI

Datasets are the first step of the machine learning lifecycle—to get started you need data, and lots of it. Vertex AI currently supports managed datasets for four data types—image, tabular, text, and videos. 

Image

Image datasets let you do:

• Image classification—Identifying items within an image.
• Object detection—Identifying the location of an item in an image
• Image segmentation—Assigning labels to pixel level regions in an image.

To ensure your model performs well in production, use training images similar to what your users will send. For example, if users are likely to send low quality images, be sure to have blurry and low resolution images in your data set. Don’t forget to include different angles, backgrounds, and resolutions. We recommend you include at least 1,000 images per label (item you want to identify), but you can always get started with 10 per label. The more examples you provide, the better your model will be.

Tabular

Tabular datasets enable you to do:

• Regression—Predicting a numerical value.
• Classification—Predicting a category associated with a particular example.
• Forecasting—Predicting the likelihood of sudden events or demands.

Tabular data sets support hundreds of columns and millions of rows. 

Text

With text datasets, you can do:

• Classification—Assigning one or more labels to an entire document.
• Entity extraction—Identifying custom text entities within a document, like “too expensive” or “great value”.
• Sentiment analysis—Identifying the overall sentiment expressed in a block of text, for example, if a customer was happy or upset or frustrated.

Video

Video datasets enable:

• Classification—Labeling entire videos, shots, or frames.
• Action recognition—Identifying clips video clips where specific actions occur.
• Object tracking—Tracking specific objects in a video.

Creating and managing datasets in Vertex AI

Now that we’ve covered the different types of data you can use, let’s shift to creating and managing those datasets. In the Cloud Console, go to Vertex AI dashboard page and click Datasets, then click Create Project.

Say you want to classify items within a set of photos. Create an image dataset and select image classification. You can import files directly from your computer, which will be stored in Cloud Storage. Then, you’ll need to add the corresponding labels (items you want to identify) for your images. If you already have labels, you can use the Import File option to import a CSV with your image URLs and their labels. If your data is not labeled and you would like human help to label it, you can use the Vertex AI data labeling service. Once the files are uploaded, you can create labels and assign them to the images. You can also analyze the images in the data set, the number of images per label, and a few other properties. 

Depending on the type of data you use, your options might vary slightly. For example, if you want to use tabular data, you could upload a CSV file from your computer, use one from Cloud Storage, or select a table from BigQuery directly. Once you select the table, the data is available for analysis.

More to come

This concludes our overview of creating and managing datasets in Vertex AI. In a future installment, we’ll go over the next phase of the machine learning workflow: building and training ML models. 

If you enjoyed this post, keep an eye out for more AI Simplified episodes on YouTube. In the meantime, here’s where you can learn more about Vertex AI.

Presently, every government agency has to take a hard look at their data capabilities and decide whether their current infrastructure supports their workflow. For many, it doesn’t. Most data systems are developed with a strict set of parameters in mind before implementation, which can limit flexibility and long-term use. Particularly during a crisis, flexible “living systems” offer tremendous advantages as they’re able to change capacity rapidly. Building living data systems with the cloud in mind allows organizations to respond to a changing world with confidence.

Last summer, the Government Business Council conducted a survey of government employees to understand the impacts of data efficiency on government operations. The report Built to Last: A Survey on Organizational Data Efficiency in Times of Crisis offers key insights into organizational efficacy and whether organizations can adapt to a crisis at speed. It also highlights differences between traditional data systems and living data systems. 

Data needs to be readily available

When the pandemic first hit, many agencies needed to create or transition their systems to allow employees to work remotely. This change tested the limits of existing data systems. Even after finding a cloud service provider, agencies encountered the challenges of migrating their data to the cloud. 

Government organizations had decades of data stored in paper records. Most have been working to transfer these records to a digital format, but the process has been slow. They are also faced with collecting sizable amounts of data in real time from their ongoing services,  which involves interfacing with the public, external vendors, or third-party institutions. 

Building the cloud into a flexible data system can solve both issues. Old records can be digitized and given an easy-to-access home for those who need them. Incoming data, both internal and external, can be made accessible as well. Migrating data to the cloud also doubles as a way to create backups of raw data, adding an extra layer of security. Most importantly, building in the cloud unlocked the capacity to scale when demand rises. 

Data should be updated in real-time

One of the key takeaways from the Government Business Council report is the fact that agencies are better able to adapt at speed when data efficiencies are higher. 74% of organizations with pandemic related functions reported a moderate to severe impact to their jobs at the onset of the pandemic. Of those organizations, the ones reporting their data efficiency as “very good” have largely already recovered. That adaptability directly affects an agency’s ability to make informed decisions during a time of a crisis.

Having a real-time data solution in place lets agencies make near real-time decisions. A great example of this from early in the pandemic is vaccine distribution. Google Cloud supported multiple states, such as the State of Wyoming, in distributing vaccines efficiently while handling challenges such as reaching rural populations. Data systems that gathered real-time patient data made a difference in the number of vaccines distributed. Knowing population data and patient risk factors enabled quick and effective decision-making.

A global pandemic is far from the only crisis that needs effective data analytics. Natural disasters, food deserts, public health issues, and more can all be handled more efficiently by having real-time data at hand. Effective data analytics systems are the digital equal of “having your ear to the ground” in each community. They provide valuable insights into what people  need.

Data needs to be accessible and easy to use

Making data easy to work with and understand sets phenomenal data systems apart from functional ones. Having data in the cloud is a great first step, but agencies need to be able to easily access and quickly use the data to accomplish their goals. This is where traditional data systems fail most often. Traditional IT systems and data strategies are designed for a specific purpose, usually identified before development and implementation begin. That means that when the data living in those systems needs to be used differently, adapting to new requirements can be difficult. 

Data can often feel “locked” in traditional systems; the data is there, but there’s no way to get to it or work with it in a way that meets the needs of a crisis. Flexible data systems address this by allowing for greater accessibility. Google Cloud, for example, has customizable tools, such as Contact Center AI and Document AI, which let agencies work with data in ever-changing ways. This also produces greater data transparency since data sets can be worked with and accessed more easily.

Governments need to respond to the changing needs of their constituents in emergencies. While traditional data systems can handle slowly shifting demands on the system, they do not serve agencies well in a crisis. When urgency, accuracy, and accessibility all matter, flexible systems rise to the challenge. The pandemic has pushed agencies to adapt in real time, and many have realized they need a system that adapts with them.

Google Cloud has a suite of tools to create integrated data ecosystems. These ecosystems can scale with increasing demand, meet dynamic development needs, and adapt to a changing landscape. Data-first decision-making is a core tenet of “living data systems.” Google Cloud data systems have handled everything from administering vaccines to detecting fraud. In each of these applications, a core tenet of data-first decision making was implemented at scale. 

For more insights on how flexible data systems help the public sector, download the full report “Built to Last: A Survey on Organizational Data Efficiency in Times of Crisis.”

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