At Google Cloud, our customers are at the forefront of digital transformation—launching entirely new businesses and products built in the cloud, redefining entire industries with data and artificial intelligence, delivering innovative new consumer experiences, or committing to sustainable new ways of doing business.

We’re committed to our customers’ success, and over the past two years we’ve invested significantly in providing ongoing and integrated support through our customer care portfolio, including launching new Premium and Mission Critical Support offerings that allow us to monitor, prevent, and mitigate impacts quickly, while delivering the fastest response times in the industry.

Today, I’m proud to unveil several new resources and offerings for our customers, including a new Custom AI Solutions practice, new Global Delivery Centers, expanded Executive Briefing Centers, and new leaders to help continue to drive our organization forward.

Helping businesses innovate with a new Custom AI Solutions practice

We are investing in services and offerings to help our customers innovate and drive innovative change to business processes, culture, and customer experiences with Google Cloud products and services. Artificial intelligence (AI) and machine learning (ML) technologies are foundational for many such digital transformations, and to help customers create real business value with these technologies, we’re excited to launch a Custom AI offering to address our customers’ most critical innovation needs. 

AI and ML technologies are foundational to digital transformations, yet they are not “one size fits all.” Each customers’ problems and opportunities are unique; a rideshare company will use AI differently than a brick and mortar retailer, or a healthcare company, or an insurance firm. To help organizations deploy AI and ML more effectively, we’re launching a new Custom AI Solutions practice, offering customers custom-built AI and ML solutions built into Vertex AI; access to Google’s engineering expertise; and predictable, subscription-based pricing. 

Our teams are already partnering closely with early customers to build and deploy custom AI solutions. For instance, USAA, the large North American insurer, is using Google Cloud ML to process near-real-time damage estimates based on digital images to create a streamlined operations experience.

Learn more about our Custom AI Solutions practice offering here, and how we work together with our customers here. To get in touch, please contact our sales team.

Launching new Global Delivery Centers

Our drive to digitally transform our customers’ businesses is often manifested through our Global Delivery Centers, which expand our professional consulting and emerging practices capabilities available to customers and partners around the world.

The teams at our Global Delivery Centers help customers get up-and-running on Google Cloud quickly and cost effectively, and consult with customers to rapidly build capacity in areas like data analytics, hybrid and multi-cloud, artificial intelligence, and machine learning. More importantly, they help customers successfully execute projects in support of their most mission-critical business objectives. Critically, these centers also help our global partner ecosystem quickly ramp their Google Cloud practices, and get fast, expert consultation for their customers too.

In 2022, we aim to triple the size of our Global Delivery Center teams in Argentina, Poland, and India. In addition, we’ll invest in building deep Google Cloud talent in Mexico and Portugal, furthering our commitment to the industry’s best expert consultation for customers and partners around the world.

Expanding our Executive Briefing Centers footprint

In addition to our Global Delivery Centers, we’re also pleased to expand our resources and facilities that enable digital and face-to-face meetings and working sessions with our customers. This year, we will launch four new Executive Briefing Centers, located on Google Cloud campuses in London, Paris, Singapore, and Munich. 

These centers provide an opportunity to listen to our customers, share the best of Google Cloud’s solutions, and inspire digital transformation, in conversations facilitated by Google Cloud leadership, engineers, and industry experts. By bringing this experience to our customers in-region we can foster deeper partnerships and develop cloud solutions that meet their requirements for security, privacy, and digital sovereignty without compromising on functionality or innovation. 

Adding new leadership to enable customer success

Finally, I’m excited to welcome two new leaders to Google Cloud on the Customer Experience team, who will help scale our Delivery Centers and deliver exceptional experiences for our customers.

Heading our Global Delivery Center experience is Sunil Rao. Sunil comes to Google Cloud from Accenture, where he spent 18-plus years working with large technology customers across many industry verticals and managed large global teams in Accenture Advanced Technology Center. Sunil will also lead our Technical Onboarding Center, which helps businesses around the world get up to speed with technologies that are critical to understanding customer and business process contexts, and delivering great experiences.

Additionally, Lee Moore is joining Google Cloud to lead Customer Experience in North America. Lee spent nearly 30 years at Accenture in various leadership positions, including services integration for complex problem-solving, product development across a number of industry verticals, and building long-term customer relationships.

You’ll be hearing much more from us in the coming months, as we build out even more powerful and effective cloud-based services and offerings, work with customers to deliver new analytics- and AI-based tools and services, and work with our growing list of partners to help ensure customer success, across the globe.

At Google Cloud, we’re committed to making generative AI useful for everyone. Doing so requires more than making powerful foundation models available to businesses, governments, and developers. Models also need to be backed by platforms that make adoption faster and safer, with onramps to meet organizations wherever they are, regardless of their software or data science expertise. 

Today, we’re excited to announce general availability of Generative AI support on Vertex AI, giving customers access to our latest platform capabilities for building and powering custom generative AI applications. With this update, developers can access our text model powered by PaLM 2, Embeddings API for text, and other foundation models in Model Garden, as well as leverage user-friendly tools in Generative AI Studio for model tuning and deployment. Backed by enterprise-grade data governance, security, and safety features, Vertex AI can make it easier than ever for customers to access foundation models, customize them with their own data, and quickly build generative AI applications.

Vertex AI powers generative AI model customization for enterprise developers, data scientists, and everyone in between 

Foundation models are the starting point for creating customized generative AI applications—but models alone are not sufficient. That’s why in March, we announced Generative AI support on Vertex AI, the biggest-ever update to our machine learning platform, and began working with trusted testers. Now generally available to customers, Model Garden and Generative AI Studio leverage Google Cloud’s tight partnership with Google Research and Google DeepMind, making it easy for developers and data scientists to use, customize, and deploy models. 

Model Garden lets customers access and experiment with foundation models from Google and its partners, with over 60 models available and many more to come. In addition to making Model Garden, PaLM 2, and Embeddings API for text generally available, we’re also making our recently-announced Codey model for code completion, generation, and chat available for public preview. 

Along with these and other foundation models, Vertex AI offers a full ecosystem of tools to help builders tune, deploy, and govern models in production. For example, in May, we were the first enterprise ML platform to provide Reinforcement Learning with Human Feedback, or RLHF, which helps improve model usefulness and reduce cost. We’ve also upgraded Vertex AI’s suite of MLOps tools for model development and maintenance for customers who need to manage large models. With Generative AI Studio generally available, customers can now leverage an even wider range of tools, including multiple tuning methods for large models, that can significantly accelerate development of custom generative AI applications. 

We’re already seeing innovative results from early adopters via our trusted tester program and preview period. For example, leading global airline supplier GA Telesis is using our PaLM model on Vertex AI to build a data extraction solution that automatically synthesizes email orders and provides customers a quote. This eliminates the need for their sales teams to manually cross-reference emails with inventory availability. GitLab is leveraging our Codey model on Vertex AI for their “Explain this Vulnerability” feature, which gives their users a natural language description of code vulnerabilities, along with recommendations for resolving them. Canva, the visual communication platform, is using Google Cloud’s rich generative AI capabilities in language translation to better support its non-English speaking users, letting users easily translate presentations, posters, social media posts, and more into over a hundred languages. The company is also testing ways that Google’s PaLM technology can turn short video clips into longer, more compelling stories. 

And today, we’re pleased to share that Typeface, and DataStax are also building new generative AI capabilities with Vertex AI.    

Now is the time to build 

These announcements add to our news yesterday that we’ve added expanded access to Enterprise Search on Generative AI App Builder (Gen App Builder), allowing businesses to create custom chatbots and search engines that combine generative AI with Google’s semantic search technologies. Gen App Builder offers out-of-box solutions to common generative AI use cases, Vertex AI’s expansive platform capabilities can accelerate wide-ranging innovation, and growing ecosystem support from partners help our customers build freely. Together, these technologies and partnerships mean the full spectrum of developers and data scientists, from novices to seasoned experts, can build generative AI apps with enterprise-ready services on Google Cloud. 

As with our entire Cloud portfolio, Vertex AI and Gen App Builder help give customers complete control over their data; it doesn’t need to leave the customer’s tenant, is encrypted both in transit and at rest, and is not shared or used to train Google models. Google rigorously evaluates our new models to ensure they meet our Responsible AI Principles, and all of our generative AI offerings include the user security, data management, and access controls Google Cloud customers have come to expect. 

We’re grateful to our trusted testers for their integral role in bringing Generative AI support on Vertex AI to market, and we look forward to seeing what customers across all industries create with our growing catalog. To learn more about Google Cloud’s generative AI products, visit our solutions page, and to keep up with our latest AI news, don’t miss “The Prompt” or our generative AI primer for executives on Transform with Google Cloud.

Whether they meet customers online, offline, or in some combination, retailers share a big problem: How can they offer the right choices, when and how the customer wants, without overwhelming (and often losing) the buyer?

More than anything, this is an information problem. As such, it’s a good candidate for using artificial intelligence (AI) for greater success. Here’s how Richemont tackled the problem.

Richemont owns a portfolio of leading luxury goods brands, recognized for their distinctive heritage, craftsmanship and creativity. It has strengths and specialties in jewelry (Cartier, Van Cleef & Arpels), luxury watches (IWC, Jaeger-LeCoultre, Panerai, Vacheron Constantin), and fashion & accessories (Chloé, Montblanc, dunhill).

People shop for such goods in a number of ways, from online searching to individual meetings in boutiques, and Richemont must be prepared for every context. Understanding which shoppers are likely to buy or repurchase, when to engage directly, and what creation to suggest enables sales associates to spend quality time with clients, engaging at the right time with meaningful advice. Richemont solves these retail challenges with an integrated Client Platform leveraging Google Cloud and its AI/ML capabilities.

Enticing peoples’ desires with Machine Learning

Richemont began by posing two questions:

1. Which prospects or clients need extra attention? Specifically, who is likely to convert or to repurchase?
2. What would be meaningful items to suggest to each client and prospect?

Both questions were addressed with machine learning algorithms. Their challenges included deploying and monitoring algorithms at scale for several brands across the globe, while addressing the specific business needs for each brand. For instance, it may be more relevant to recommend in-season items for fashion brands, while for watchmakers it is more about cross-fertilization across each brand’s iconic creations.

This graph summarizes the prediction process implemented by Richemont:

Engagement data (email opened, clicked, SMS/MMS, website visits…) was found crucial to predict conversion of prospects for whom per definition no transaction history is available. For website interactions Richemont leverages the Google x Salesforce Connector.

To deploy the Machine Learning algorithms and to monitor them, Richemont leveraged Vertex AI, along with BigQuery, Cloud Functions and Google Storage, all orchestrated with Google Cloud Composer.

The role of product recommendation algorithms

Richemont used the deep learning library TensorFlow Recommenders to perform the product recommendation tasks. This library enables companies to build state of the art deep learning algorithms to achieve relevant and robust predictions.

Unlocking client value with integrated technology

Richemont’s innovations show how technology that considers many parts of the customer experience creates more value. In this case, the company used in store applications to invite people with a strong propensity to buy for boutique visits, while others at a different point in the purchasing journey were offered different options more suited to their tastes and inclinations.This solution, now deployed across 11 brands in over 25 countries, shows just one way that AI can improve customer experience, for better customer loyalty.

Key to the process, here and elsewhere, is the way a retailer and its partners put customer understanding at the center of the process. As AI becomes more important not only in retail, but in every industry, this human understanding will become even more important as a fundamental organizing principle. Much is changing, but once again, the winners will be the companies that focus best on their customers.

If you’ve been exploring recently-launched consumer generative AI tools like Bard and thinking about how to build similar experiences for your business, Generative AI App Builder, or Gen App Builder for short, is here to get you started.

Gen App Builder is part of Google Cloud’s recently announced generative AI offerings and lets developers, even those with limited machine learning skills, quickly and easily tap into the power of Google’s foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications. 

“Google Cloud’s leading AI technology enables STARZ customers to discover more relevant content, increasing engagement with, and the likelihood of completing the content served to them,” says Robin Chacko, EVP Direct-to-Consumer, STARZ. “We’re excited about how generative AI-powered search will help users find the most relevant content even easier and faster.”

Gen App Builder is exciting because unlike most existing generative AI offerings for developers, it offers an orchestration layer that abstracts the complexity of combining various enterprise systems with generative AI tools to create a smooth, helpful user experience. Gen App Builder provides step-by-step orchestration of search and conversational applications with pre-built workflows for common tasks like onboarding, data ingestion, and customization, making it easy for developers to set up and deploy their apps. With Gen App Builder developers can: 

• Build in minutes or hours. With access to Google’s no-code conversational and search tools powered by foundation models, organizations can get started with a few clicks and quickly build high-quality experiences that can be integrated into their applications and websites. 
• Combine the power of foundation models with information retrieval to find relevant, personalized information. Enterprises can build apps that understand user intent via natural language, and surface the right information with associated citations and attributions from a company’s public and private data. They can also fully control what data their applications access and the content or topics they want to address.
• Build multimodal apps that can respond with text, images, and other media. Gen App Builder supports not just text, but also other modalities such as images and videos. It allows developers to build apps using a combination of text and images as inputs to find information across documents, photos, and video content, enabling richer customer interactions. 
• Combine natural conversations with structured flows. Developers can granularly blend the output of foundation models with controls to ground answers in enterprise content, and step-by-step conversation orchestration to guide customers to the right answers.
• Provide the ability to transact and connect to third party apps and services. Gen App Builder makes it simple to create digital assistants and bots that not only serve content, but also connect to purchasing and provisioning systems to enable transactions from the conversational UI, and escalate customer conversations to a human agent when the context demands. 

A new generation of conversational AI experiences and assistants 

Consumers of enterprise applications expect to interact with technology in a seamless, conversational way to quickly find the information they need and act on it. Gen App Builder can help reinvent these customer and employee experiences by ingesting large, complex datasets that are specific to your company–from websites, documents, and transactional systems like billing and inventory, to emails, chat conversations, and more. These AI-powered apps can synthesize information across all of these sources to provide specific, actionable responses, using only the data you have provided. 

Some of the most popular uses are in customer service, where generative apps can contribute to increasing revenue, customer satisfaction, and customer loyalty. For example, if a retail customer reaches out to modify an order, a virtual agent can help them change it to another product. The customer doesn’t even need to provide the new product name—they can just upload an image and let the agent guide them through the rest. Watch this demo to see how a retail chatbot can use multimodal capabilities to help a consumer navigate various options on the website, including giving the customer ideas on how to use the product and even helping them complete the purchase with the ability to transact within the conversational UI. This scenario could apply to multiple industries and use cases, ranging from consumer goods and public services, to finance and internal corporate systems like intranets.

Combining the power of Google-quality search with foundation models

Finding the right information from data across the organization is a critical requirement within any enterprise. Yet it can be challenging to build high-quality enterprise search experiences with existing tools. Current systems struggle to understand user intent, are difficult to implement and customize, and don’t provide a high-quality user experience. 

One of the most exciting features of Gen App Builder is the ability to combine the power of Google-quality search with generative AI to help enterprises find the most relevant and personalized information when they need it. With Gen App Builder, enterprises can build conversational search experiences across their public and private data in minutes or hours with no coding experience. 

Enabling multimodal search across text, images and video within the enterprise is a key aspect of the search experiences in Gen App Builder. In addition to providing high-quality search results, Gen App Builder can conveniently summarize the results and provide corresponding citations in a natural, human-like fashion. Gen App Builder also automatically extracts key information from the data and enables personalized results for users. Watch this demo to see how these capabilities can come together to transform the search experience for employees at a financial services firm. The ability to integrate Google-quality search within the enterprise’s applications means they can enjoy a new level of data utilization, drive increased process efficiencies, and provide delightful experiences to their employees and customers.

“Customers have been shopping at Macy’s for generations. Being able to deliver 360° personalization and contextual recommendations will help ensure that Macy’s is still providing future generations of shoppers with a seamless, exceptional experience,” said Bennett Fox-Glassman, Senior Vice-President, Customer Journey, Macy’s. “We’ve already realized an increase in revenue per visit and conversion rates had great success using Google Cloud’s AI technology and are looking forward to exploring how these latest announcements bring together Natural Language Processing and Generative AI capabilities to deliver next-gen search and conversational experiences for our customers.”

The ability to intuitively interact with complex data across a variety of sources allows organizations to better serve their customers and deliver more relevant offerings. Combined with conversational and fulfillment abilities, the potential for improving customer engagement and employee productivity is immense. We’re excited to see how developers and enterprises use a mix of these capabilities to power new experiences and revenue opportunities.

If you’re interested in a closer look at the Gen App Builder, tune into this session at the Data Cloud & AI Summit. Take a step forward to getting hands-on and join the waitlist for our trusted tester program. And finally, bookmark our generative AI landing page to keep abreast of the latest news, updates and possibilities from this exciting new world of Gen Apps.

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.