🎧 Prefer to listen? Check out this episode on the Google Cloud Reader podcast

If you’re anything like me, you love reading, but also appreciate that sometimes your eyes need to be doing other things; whether it’s finding your exit off the highway, or keeping your puppy from destroying the couch.

And sometimes the thought of sitting down to read something just feels like it’s going to take valuable multi-tasking time away from my day. I know, I know, multitasking can be frowned upon, but it’s the way I live a good chunk of my life, and it’s working out so far. And while I’m not alone in my multitasking, I’m also not alone in my desire for a non-visual way to get this content, or any content.

*Google Cloud Reader enters the chat*

Google Cloud Reader is a podcast that lets you listen to the Google Cloud Blog posts that aren’t as dependent on visuals. This means they’re articles that are, or are adapted to be, less focused on graphs, or code samples, and instead describe the meaning behind those visual aids. 

It’s an easy, audible way to absorb content around all things new in Cloud, while still being able to make sure Ruthie doesn’t eat my work from home equipment. 

So by now you’re probably thinking “OK, so you started a podcast during the pandemic, even though you definitely seemed like the type to start making sourdough”—and you’re right. My 53 plants agree with you. But rest assured, one can listen to an episode of this podcast *while* creating a macramé plant hanger, or waiting for bread to rise—multitasking, am I right?

We’re a little over 50 episodes/macrame plant hangers in, so you should check it out (Ruth and I would appreciate it).

Some of my personal favorites 

• Beginners Guide to Painless Machine Learning – Learn how to get started with Google Cloud AI tools
• Introducing GKE Autopilot: A Revolution in Managed Kubernetes – Learn more about GKE Autopilot, a revolutionary mode of operations for managed Kubernetes that lets you focus on your software, while GKE Autopilot manages the infrastructure.
• Cook up your own ML recipes with AI Platform – ​​Learn about Mars Wrigley’s new ML-inspired recipe experiment on Google Cloud and how you can get started with your own.
• Recovering Global Wildlife Populations using ML – Review Google’s Wildlife Insight’s ML project and help users create an image classification model for motion-sensor cameras (called camera traps) used to help protect wildlife in an non-invasive way by collecting and tagging species via pictures.

Let me know your favorite episodes, and what other articles you’d like to hear on Twitter @jbrojbrojbro!

No matter why you prefer an audio format, we’ve got you covered; Google Cloud Reader, where we read the tech blog for you, and to you.

Get all the Google Cloud Reader on your favorite podcast platform, including Google Podcasts, Apple Podcasts, and Spotify.

The app economy has enabled a huge range of unique business models to flourish. One such model is online food ordering and delivery services, in which apps leverage geo-location data to aggregate local food choices and offer personalized options to consumers.

A leading company in this space is Just Eat. Launched in the UK in 2001 with a vision of ‘serving the world’s greatest menu. Brilliantly.’ The company has capitalized on the popularity of online food delivery and grown its presence across 12 markets. 

Just Eat acts as an intermediary between take-out food outlets and hungry customers, giving local restaurants access to a broader base of potential diners, while providing consumers with an easy and secure way to order and pay for food from their favourite restaurants.

Today the company helps 27 million customers find food from more than 112,000 restaurants—everything from homemade Italian pasta, to Chinese noodle bowls, to fish-and-chips. 

Data is the fuel of Just Eat’s rapid growth, but it wasn’t always looked at that way. In its early days, Just Eat struggled with the deluge of information and faced fragmentation across its systems. In fact, the company realized its legacy data vendor wasn’t capable of ingesting 90 percent of the data produced by its food platform. This was incredibly frustrating for Just Eat’s analysts and data scientists, who had to waste time cleaning up sources instead of leveraging the data to create a better user experience. 

Just Eat turned to Google Cloud, and now uses machine learning (ML) to power sophisticated consumer recommendations on both its app and website. It also makes heavy use of features offered by Google Cloud Platform, including BigQuery for running analytics on its customer data set and Cloud Pub/Sub for messaging app users with relevant offers in real-time. 

Having all of Just Eat’s data in one platform has translated into real value for its customers. With Google Cloud tools, Just Eat has created its own proprietary Customer Ontology framework, which today contains 5.5 billion features that better understand consumers’ behavior and food habits, and provides insights into previous visits.

Just Eat recently created an “Adventurous Index” to map its customers according to their ordering habits, enabling them to tailor their marketing and user experiences. For example, mid-adventurous customers are shown a choice of restaurants that serve their most ordered cuisine, while adventurous customers can choose from restaurants that serve a wider variety. This not only has prompted consumers to be more adventurous with their choices, but also has led to more business at a more diverse set of restaurants.

Matt Cresswell, Director of Customer Platforms at Just Eat said that Google Cloud has become integral to its product delivery: “Consumer food choice is a hugely nuanced topic. We know that individuals have their own unique journeys when they use Just Eat. We’ve sought to create a truly one-to-one relationship with every customer. The changes we’ve made to the platform mean they can access the dishes they enjoy at the touch of a fingertip, and find inspiration to discover new dishes they’ll love. We’re grateful to Google Cloud for helping us support our customers on their culinary explorations.”

Alphabet CEO Sundar Pichai has compared the potential impact of artificial intelligence (AI) to the impact of electricity—so it may be no surprise that at Google Cloud, we expect to see increased AI and machine learning (ML) momentum across the spectrum of users and use cases.

Some of the momentum is more foundational, such as the hundreds of academic citations that Google AI researchers earn each year, or products like Google Cloud Vertex AI accelerating ML development and experimentation by 5x, with 80% fewer lines of code required. Some are more concrete, like mortgage servicer Mr. Cooper using Google Cloud Document AI to process documents 75% faster with 40% cost savings; Ford leveraging Google Cloud AI services for predictive maintenance and other manufacturing modernizations; and customers across a wide range of industries deploying ML platforms atop Google Cloud.

Together, these proof points reflect our belief that AI is for everyone, and that it should be easy to harness in workflows of all kinds and for people of all levels of technical expertise. We see our customers’ accomplishments as validation of this philosophy and a sign that we are taking away the right things from our conversations with business leaders. Likewise, we see validation in recognition from analysts, which recently includes Google being named a Leader by

Gartner® in the 2022 Magic Quadrant™ for Cloud AI Developer Services report

Forrester in the Forrester Wave™: AI Infrastructure, Q4 2021 report, the Forrester Wave™: Document-Oriented Text Analytics Platforms, Q2 2022 report, and The Forrester Wave™: People-Oriented Text Analytics Platforms, Q2 2022 report

In June, we talked about four pillars that guide our approach to creating products for MLOps and to accelerate development of ML models and their deployment into product. In this article, we’ll look more broadly at our AI and ML philosophy, and what it means to create “AI for everyone.”

AI should be for everyone

One of the pillars we discussed in June was “meeting users where they are,” and this idea extends far beyond products for data scientists. Technical expertise should not be a barrier to implementing AI—otherwise, use cases where AI can help will languish without modernization, and enterprises without well-developed AI practices will risk falling behind their competitors.

To this end, we focus on creating AI and ML services for all kinds of users, e.g.:

• DocumentAI, Contact Center AI, and other solutions that inject AI and ML into business workflows without imposing heavy technical requirements or retraining on users;
• Pre-trained APIs, ranging from Speech to Fleet Optimization, that let developers leverage pre-trained ML models and free them from having to develop core AI technologies from scratch;
• BigQuery ML to unite data analysis tasks with ML;
• AutoML for abstracted and low-code ML production without requiring ML expertise;
• Vertex AI to speed up ML experimentation and deployment, with every tool you need to build deploy and the lifecycle of ML projects
• AI Infrastructure options for training deep learning and machine learning models cost effectively. Including Deep Learning VMs optimized for data science and machine learning tasks and AI accelerators for every use case, from low-cost inference to high-performance training.

It’s important to provide not only leading tools for advanced AI practitioners, but also leading AI services for users of all kinds. Some of this involves abstracting or automating parts of the ML workflow to meet the needs of the job and technical aptitude of the user. Some of it involves integrating our AI and ML services with our broader range of enterprise products, whether that means smarter language models invisibly integrated into Google Docs or BigQuery making ML easily accessible to data analysts. Regardless of any particular angle, AI is turning into a multi-faceted, pervasive technology for businesses and users the world over, so we feel technology providers should reflect this by building platforms that help users harness the power of AI by meeting them wherever they are.

How we’re powering the next generation of AI

Creating products that help bring AI to everyone requires large research investments, including in areas where the path to productization may not be clear for years. We feel a foundation in research combines with our focus on business needs and users to inform sustainable AI products that are in keeping with our AI principles and encourages responsible use of AI.

Many of our recent updates to our AI and ML platforms began as Google research projects. Just consider how DeepMind’s breakthrough AlphaFold project has led to the ability to run protein prediction models in Vertex AI. Or how research into neural networks helped create Vertex AI NAS, which lets data science teams train models more accurately with lower latency and power requirements.

Research is crucial, but also only one way of validating an AI strategy. Products have to speak for themselves when they reach customers, and customers need to see their feedback reflected as products are iterated and updated. This reinforces the importance of seeing customer adoption and success across a range of industries, use cases, and user types. In this regard, we feel very fortunate to work with so many great customers, and very proud of the work we help them accomplish.

I’ve already mentioned Ford and Mr. Cooper, but those are just a small sampling. For example, Vodafone Commercial’s “AI Booster” platform uses the latest Google technology to enable cutting-edge AI use cases such as optimizing customer experiences, customer loyalty, and product recommendations. Our conversational AI technologies are used by companies ranging from Embodied, whose Moxie robot helps children overcome developmental challenges, to HubSpot connecting meeting notes to CRM data. Across our products and across industries around the world, customer stories grow by the day.

We also see validation in our partner network. As we noted in the pillars discussed in June, partners like Nvidia help us to ensure customers have freedom of choice when building their AI stacks, and partners like Neo4j help our customers to expand our services into areas like graph structures. Partners support our mission to bring AI to everyone, helping more customers use our services for new and expanded use cases.

Accelerating the momentum

Overall, to create products that reflect AI’s potential and likely future ubiquity, we have to take all of the preceding factors, from research to customer and analyst conversations to working with partners, and turn them into products and product updates. We’ve been very active over the last year, from the launch of Call Center AI Platform in March, to the new Speech model we released in May, to a range of announcements at the Google Cloud Applied ML Summit in June. We have much more planned in coming months, and we’re excited to work with customers not just to maintain the pace of AI momentum, but to accelerate it. To learn more about Google Cloud’s AI and ML services, visit this link or browse recent AI and ML articles on the Google Cloud Blog.

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