Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. 

Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

Week of May 24-May 28 2021

• Google Cloud for financial services: driving your transformation cloud journey–As we welcome the industry to our Financial Services Summit, we’re sharing more on how Google Cloud accelerates a financial organization’s digital transformation through app and infrastructure modernization, data democratization, people connections, and trusted transactions. Read more or watch the summit on demand.
• Introducing Datashare solution for financial services–We announced the general availability of Datashare for financial services, a new Google Cloud solution that brings together the entire capital markets ecosystem—data publishers and data consumers—to exchange market data securely and easily. Read more.
• Announcing Datastream in Preview–Datastream, a serverless change data capture (CDC) and replication service, allows enterprises to synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency to support real-time analytics, database replication, and event-driven architectures. Read more.
• Introducing Dataplex: An intelligent data fabric for analytics at scale–Dataplex provides a way to centrally manage, monitor, and govern your data across data lakes, data warehouses and data marts, and make this data securely accessible to a variety of analytics and data science tools. Read more. 
• Announcing Dataflow Prime–Available in Preview in Q3 2021, Dataflow Prime is a new platform based on a serverless, no-ops, auto-tuning architecture built to bring unparalleled resource utilization and radical operational simplicity to big data processing. Dataflow Prime builds on Dataflow and brings new user benefits with innovations in resource utilization and distributed diagnostics. The new capabilities in Dataflow significantly reduce the time spent on infrastructure sizing and tuning tasks, as well as time spent diagnosing data freshness problems. Read more.
• Secure and scalable sharing for data and analytics with Analytics Hub–With Analytics Hub, available in Preview in Q3, organizations get a rich data ecosystem by publishing and subscribing to analytics-ready datasets; control and monitoring over how their data is being used; a self-service way to access valuable and trusted data assets; and an easy way to monetize their data assets without the overhead of building and managing the infrastructure. Read more.
• Cloud Spanner trims entry cost by 90%–Coming soon to Preview, granular instance sizing in Spanner lets organizations run workloads at as low as 1/10th the cost of regular instances, equating to approximately $65/month. Read more.
• Cloud Bigtable lifts SLA and adds new security features for regulated industries–Bigtable instances with a multi-cluster routing policy across 3 or more regions are now covered by a 99.999% monthly uptime percentage under the new SLA. In addition, new Data Access audit logs can help determine whether sensitive customer information has been accessed in the event of a security incident, and if so, when, and by whom. Read more.
• Build a no-code journaling app–In honor of Mental Health Awareness Month, Google Cloud’s no-code application development platform, AppSheet, demonstrates how you can build a journaling app complete with titles, time stamps, mood entries, and more. Learn how with this blog and video here.
• New features in Security Command Center—On May 24th, Security Command Center Premium launched the general availability of granular access controls at project- and folder-level and Center for Internet Security (CIS) 1.1 benchmarks for Google Cloud Platform Foundation. These new capabilities enable organizations to improve their security posture and efficiently manage risk for their Google Cloud environment. Learn more.
• Simplified API operations with AI–Google Cloud’s API management platform Apigee applies Google’s industry leading ML and AI to your API metadata. Understand how it works with anomaly detection here.
• This week: Data Cloud and Financial Services Summits–Our Google Cloud Summit series begins this week with the Data Cloud Summit on Wednesday May 26 (Global). At this half-day event, you’ll learn how leading companies like PayPal, Workday, Equifax, and many others are driving competitive differentiation using Google Cloud technologies to build their data clouds and transform data into value that drives innovation. The following day, Thursday May 27 (Global & EMEA) at the Financial Services Summit, discover how Google Cloud is helping financial institutions such as PayPal, Global Payments, HSBC, Credit Suisse, AXA Switzerland and more unlock new possibilities and accelerate business through innovation. Read more and explore the entire summit series.
• Announcing the Google for Games Developer Summit 2021 on July 12th-13th–With a surge of new gamers and an increase in time spent playing games in the last year, it’s more important than ever for game developers to delight and engage players. To help developers with this opportunity, the games teams at Google are back to announce the return of the Google for Games Developer Summit 2021 on July 12th-13th. Hear from experts across Google about new game solutions they’re building to make it easier for you to continue creating great games, connecting with players and scaling your business. Registration is free and open to all game developers. Register for the free online event at g.co/gamedevsummit to get more details in the coming weeks. We can’t wait to share our latest innovations with the developer community. Learn more.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}

If you’re here you’re probably wondering: what on Earth is the connection between muffins and machine learning, and what is National Muffin Day? To understand this, let’s start with National Muffin Day: an annual holiday co-founded by Jacob and his friend Julia Levy in 2015 to bake muffins and raise money for homelessness. National Muffin Day will occur on Sunday, February 20 this year. For more information on how to participate in National Muffin Day (which involves delicious baked goods and donations to people in need), please see the information at the bottom of this post. Last year, a colleague connected Jacob with Sara, who had done several baking projects that used machine learning to generate new recipes. They decided to collaborate for this year’s National Muffin Day, adding a new muffin recipe created with the help of machine learning.

In this post, we’ll explain how Sara used Google Cloud to develop a new muffin recipe, show you how you can participate virtually in National Muffin Day, and of course—share the recipe.

Machine learning for muffins

At its core, machine learning is the process of finding patterns in data and using those patterns to make predictions on new data. After a lot of baking over the past few years, Sara learned that baking is also based on patterns. For example, the ratio of flour, fat, liquid, and sugar that make up a cookie is very different from the ratio of those ingredients for a bread, a pie crust, or a muffin. She used that discovery to create a recipe for a hybrid cake + cookie, and a cake filled with Maltesers. Next up: muffins! 

The first step was figuring out how to translate the task of generating a new muffin recipe into a machine learning task. To solve this, she planned to use numerical data on the amounts of different ingredients in a muffin recipe to train the model. Sara considered two types of models for this task: classification and regression. A classification model would categorize muffin recipes into different muffin types based on their ingredient amounts, and a regression model would do the reverse: take a type of muffin and return the amount of each ingredient needed to make it. She decided to build a regression model, since it would be more fun for the model to return ingredient amounts, rather than tell you which type of muffin recipe you’re already making. 

Implementing this first required identifying a few muffin categories and collecting recipe data. This presented a new challenge, since her previous baking models used categories for distinct baked goods (i.e. cakes, cookies, breads). After scouring through quite a few recipes, Sara discovered that many muffins fall into two types: those that use only traditional ingredients as their base (flour, sugar, butter, milk, etc.), and those that include an alternative ingredient, most commonly a pureed fruit, to make the base (like bananas, applesauce, or pumpkin). Using those two categories, the model would take the type of muffin as input and return the amounts of base ingredients required to make that recipe. Here, the inputs can be any values adding up to 100%:

The next step in the ML process was collecting recipe data to use for model training and narrowing down the ingredients used to train the model. Sara wanted the model to learn the combination of core ingredients that make up a muffin batter, rather than flavorings and additions like blueberries, vanilla extract, or chocolate chips. These tasty additions could be added after the model helped create the muffin batter. Once she gathered enough recipes, she removed extra ingredients for training purposes and converted ingredients from different recipes into the same unit (grams, milliliters, and teaspoons).

Building a muffin model with Vertex AI

Sara uploaded the muffin ingredient data into BigQuery, and then created a notebook instance in Vertex AI Workbench to analyze the data. With the new Workbench managed instances, you can interactively query BigQuery tables directly from your instance and copy the code to download your data to a notebook as a Pandas DataFrame:

From her notebook instance, Sara experimented with different ML frameworks and model types. She landed on a Scikit-learn regression model to solve this task, and to mimic a real-world production environment, decided to convert this workflow into a ML pipeline. Using the Kubeflow Pipelines SDK, she ran the following on Vertex Pipelines:

The first component reads the ingredient data from BigQuery and converts it into a Pandas DataFrame which is passed to the next pipeline step. In this step, we train a custom Scikit-learn model on the recipe data. Finally, this model is deployed to an endpoint in Vertex AI. To put it all together, Sara built a web app that allowed her to easily generate ingredient amounts for different muffin types. The web app uses the Vertex AI SDK to call the deployed model endpoint and return ingredient amounts.

The recipe

With a deployed recipe generation model, the only thing left to do was test recipes in the kitchen! Because the model only returns ingredient amounts, there were still many key human elements to complete the baking process: adding yummy additions to the core muffin batter, making adjustments to optimize taste, determining the method for adding ingredients, baking time, and more. After testing a few recipes generated by the model, we landed on a favorite which we’re very excited to share with you here.

Berry ML Muffins

Makes 12 muffins

Flour 285 grams (2 cups)

Granulated sugar 250 grams (1 cup)

Baking powder 2 teaspoons

Baking soda ¼ teaspoon

Salt ½ teaspoon

Cinnamon ½ teaspoon

Milk 170 ml (⅔ cup), room temperature

Butter 55 grams (¼ cup), melted and slightly cooled

Eggs 1 egg plus 1 egg white, room temperature

Canola or vegetable oil 50 grams (¼ cup)

Sour cream 50 grams (3 tablespoons + ¾ teaspoon), room temperature

Vanilla extract 1 ½ teaspoons

Blueberries or raspberries 240 grams (1 ½ cups)

Coarse sugar, like demerara or turbinado (optional for topping) 1 tablespoon 

1. Measure your three cold ingredients and allow them to come to room temperature: 1 egg + 1 egg white, sour cream, and milk. 
2. Preheat the oven to 375 F / 190 C. Line a 12-muffin tin with cupcake liners or lightly grease with baking spray.
3. In a large bowl, whisk together flour, baking powder, baking soda, salt, and cinnamon. Set aside.
4. Melt your butter in a medium heat proof bowl, and allow it to cool slightly for a few minutes. Whisk in sugar until combined. Then add egg, oil, and vanilla, milk, and sour cream and whisk until fully incorporated.
5. Pour the wet ingredients into the dry ingredients, mixing with a spatula until just combined. Be careful not to overmix, it’s ok if there are a few lumps in your batter.
6. Prepare your fruit. If you can’t decide whether to use blueberries or raspberries, divide your batter into two bowls and do both! Crush half of your fruit and fold it into the batter. Then mix in the remaining whole berries.
7. Divide the mixture evenly into the muffin tin. Optionally (but extra tasty), sprinkle the tops of each muffin with about ⅛ teaspoon of coarse sugar. Turbinado or demerara sugar work well for this. This will caramelize and add a nice texture to the tops of your muffins.
8. Bake at 375 for 22 – 24 minutes, or until a toothpick inserted in the center comes out clean. For best results, do a toothpick test in a few muffins since not all ovens have an even temperature throughout. Let the muffins cool in the muffin tin for a few minutes, then transfer to a wire rack to cool completely.
9. Enjoy!

How can you participate in National Muffin Day?

Participation in National Muffin Day is as easy as 1-2-3!

1. On February 20, Bake Muffins. It’s time to dust those muffin tins, grab your blueberries, chocolate chips, rhubarb, and favorite ingredients, and create some magical scrumdiddlyumptiousness! If you want to join Jacob in a virtual baking party, you can register here.
2. Then, Give. In non-pandemic years, we asked our bakers to personally hand muffins to hungry folks in their cities. While this is a valuable and rewarding experience, the current state of Covid means this practice is still unsafe, so we request that you refrain from doing this. Instead, if it feels safe, we encourage you to take your delicious baked goods and donate them to local homeless shelters, which can distribute them to those in need. Alternatively, you can share your muffins with friends and families and then make a donation to an organization that benefits people experiencing homelessness, like the ones listed below in step 3.
3. Share Your Muffin Pics on Social Media. We’d love to see your muffins!  Share your pictures on Twitter or Instagram with the hashtags #givemuffins, or share them to our official Facebook Event page. For each individual baker who participates, we will make donations to Project Homeless Connect, which provides much needed resources to people experiencing homelessness in San Francisco, Family promise, which supports unhoused families nationwide, and Pine Street Inn which provides resources for people experiencing homelessness in Boston. Donations will be on a per-baker basis (with up to $80 donated per baker!), so please feel free to loop in your significant others, kids, nieces and nephews, roommates, friends, and anybody else with a giving spirit who loves deliciousness!

In the last year, there’s been an upward trend in cloud adoption in India. In fact, NASSCOM finds that cloud spending in India is estimated to grow at 30% per annum to cross the US$7 billion mark by 2022.

At Google, in our conversations with customers, discussions have evolved beyond cost savings and efficiencies. While those are still very relevant reasons for adopting cloud technologies, Indian enterprises are looking to Google Cloud to help them drive digital transformation, identify new revenue generating business models, reach previously untapped consumer markets, and build customer loyalty through greater insight and personalization.

Here are some companies and their stories.

Tata Steel: Mining data and maximizing its power

Tata Steel is a great example of an established enterprise from a traditional industry that is modernizing and embracing cloud computing. With an ambition to be a leader in manufacturing in India and a digital-first organization by 2022, Tata Steel believes smart analytics is key to enhancing operational efficiency and gaining business advantage. 

To organize data from siloed systems across the organization and make it easily accessible to all employees, Tata Steel is using Cloud Search and plans to scale it to more than one million documents and 28 disparate enterprise content sources including enterprise resource planning (ERP) and SharePoint. In fact, Tata Steel is one of the first Indian enterprises to harness the power of Cloud Search to meet some of the most aggressive ingestion demands, with indexing durations reduced from weeks to seconds.

They are also leveraging Google Cloud Platform (GCP) services like Google Cloud Storage and BigQuery to build their data lake and enterprise data warehouse so they can take advantage of advanced analytics and machine learning. Managed services such as AI Platform further enable Tata Steel to manage end-to-end AI/ML workflows within the GCP console. This complements their existing on-premise reporting and analytics tools, and brings data management to the forefront of everything they do—from forecasting market demand to predictive equipment maintenance.

“Digital is not just a goal, it’s become a way of life. We are digitizing everything from the deployment of factory vehicles to improving material throughput to marketing and sales. As a result, we have petabytes of structured and unstructured data that is not only waiting to be mined, but that we can generate intelligence from to create opportunities across our multiple lines of business using GCP,” said Sarajit Jha, Chief Business Transformation & Digital Solutions at Tata Steel.

Helping L&T Financial Services reach customers in rural communities

In rural communities, quick access to financial services can make a tremendous difference to livelihoods. L&T Financial Services provides farm-equipment finance, micro loans and two-wheeler finance to consumers across rural India backed by a strong digital and analytics platform. Their digital-loan approval app, which runs on GCP, makes it significantly faster and easier for people to apply for financial assistance to purchase important things such as farming equipment and two-wheelers. It also helps rural women entrepreneurs get quicker access to funds for their businesses through micro loans.

L&T Financial found G Suite to be a far better collaborative tool to help staff work together efficiently. Employees can interact with each other in real time using Hangouts Meet, and the task of information sharing is more seamless and secure through Drive. BigQuery also helps L&T Financial Services generate behavior scorecards to track credit quality of its micro-loan customers.

“Cloud is the technology that enables us to achieve scale and reach. Today there are countless data points available about rural consumers which enable us to personalize our products to serve them better. With access to faster compute power, we can also on-board consumers more efficiently. Our rural businesses have clocked a disbursement CAGR of 60% over the past three years.” said Sunil Prabhune, Chief Executive-Rural Finance, and Group Head-Digital, IT and Analytics, L&T Financial Services.

Creating conversational connections for Digitate’s customers

Digitate, a venture of TCS (Tata Consultancy Services), has integrated Dialogflow into its flagship brand ignio, an award-winning artificial intelligence platform for driving IT operations, workload operations and ERP operations for diverse enterprises. This integration is the next step in ignio’s product development journey, and will enable users to chat or talk with ignio to detect issues, triage problems, resolve them and even predict system behavior.

“ignio combines its unique self-healing AIOps capabilities for enterprise IT and business operations with Dialogflow’s AI/ML-based, easy to use, natural and rich conversational capabilities to create an unparalleled, intuitive and feature-rich experience for our customers,” says Akhilesh Tripathi, Head of Digitate.

Indian enterprises going G Suite

The base of Indian enterprises that are making the switch to G Suite to streamline their productivity and collaboration also continues to grow. Sharechat, BookMyShow, Hero MotorCorp, DB Corp and Royal Enfield are now able to move faster within their organizations, using intelligent, cloud-based apps to transform the way they work.

A hybrid and multi-cloud future in India

IDC predicts that by 2023, 55% of India 500 organizations will have a multi-cloud management strategy that includes integrated tools across public and private clouds. (IDC FutureScape: Worldwide Cloud 2019 Predictions  — India Implications (# AP43922319). We look forward to sharing more success stories of Indian enterprises that have taken the next step in their digital transformation journey.

Why Higher Ed Needs to Go All-in on Digital

In the wake of the COVID-19 pandemic, the majority of students within the 18-24-year-old demographic now expect hybrid learning environments–even once we are beyond the pandemic. And a vast number of adult learners are seeking options that accommodate their work and family lives now that it’s clear that effective learning can indeed occur virtually. Implementing cloud technologies and achieving digital maturity within higher education will enable institutions to be innovative and responsive to evolving student preferences, while being prepared for future disruptions.

The state of digital maturity

In February and March 2021, Boston Consulting Group (BCG), in partnership with Google, surveyed U.S. higher education leaders on their views of the state of digital maturity in the higher education sector. This survey found that institutional and technology leaders strongly agreed that moving legacy IT systems to the cloud, centralizing and integrating data, and increasing the use of advanced analytics is necessary to make a successful digital transformation, and ultimately achieve digital maturity.

But what is digital maturity? Digital maturity—a measure of an organization’s ability to create value through digital delivery—focuses on three areas of technological advancement that drive large-scale innovation:

1. Using cloud infrastructure
2. Expanding access to data
3. Using that data to improve processes through advanced analytics, such as Artificial Intelligence and Machine Learning (AI/ML)

Although university leaders agree on prioritizing digital maturity, more than 55% said they considered their schools to be “digital performers” or “digital leaders.” However, only 25% of tech leaders at these universities stated that their schools regularly use data analytics. As with corporations and governments, higher education institutions face barriers to technological innovation, such as:

• Competing priorities to meet step-change goals and decentralized decision making
• Budget constraints
• Cultural resistance to change
• Tech staff skillset gaps

Still, leaders understand that the way to overcome institutional inertia is with a strong, goal-oriented vision of what is best for the institution overall. Although only a handful of schools have reached digital maturity as we define it, others can learn a great deal from their examples. Here are the top takeaways from higher education leaders who successfully transformed their institutions:

Digital solutions can improve the student journey in many ways

As digital capabilities hold the key to dealing effectively with declining enrollment and rising costs, higher ed leaders identified four goals that are critical to improving performance:

1. Improve the student journey
2. Increase operational efficiency
3. Scale computing power in advanced research
4. Innovate education delivery

The research found that technology investments can help enhance the student journey in the recruiting and retention of students, improving digital education delivery, government funding, and donations from alumni. Digital maturity can make institutions more agile and efficient in delivering education that aligns with the changing societal norms, evolving student preferences, and future disruptions. Survey participants shared that they plan to increase the use of the cloud by more than 50% over the next three years. By shifting legacy IT systems to the cloud, institutions can increase scalability, lower the cost of ownership, and improve operational agility, while offering a more secure, long-term data storage solution.

Cloud-native software-as-a-service (SaaS) solutions provide an excellent platform for centralizing data. However, institutions that attempt to “lift and shift” their legacy systems to the cloud may encounter challenges to achieving measurable improvements in data integration and cost reduction. Higher ed leaders must realize that centralizing data and transitioning to the cloud do not happen simultaneously.

Leaders who are able to articulate a strong vision and commitment will experience a more successful technology transformation. By linking their vision to specific needs, such as more effective recruiting, leaders will find their technology investments will have a more substantial return. University presidents should base their decisions about which systems to move, when, and how on desired performance outcomes.

Big visions become a reality with small steps. Small pilot projects are an excellent way to start the journey toward digital maturity. Small steps toward a significant transformation can reduce resistance to change, build positive momentum, and produce better student outcomes. Read the full report here. If you’d like to talk to a Google Cloud expert, get in touch.