In a perfect cloud world, you would host all your applications in containers running on Kubernetes and Istio, benefitting from the portability and improved resource utilization of containers, plus a robust orchestration platform with advanced application management, networking, and security functionality. This is easy to do if you’re developing a new application, but it can be hard for existing applications to take advantage of those capabilities.

Many of the applications that you may want to move to the cloud have been around a long time, and you may not have the application-specific knowledge that would be required to rewrite them to be more cloud-native—or it would be incredibly time-consuming to do so.

Another option is to lift-and-shift to a virtual machine (VM) hosting platform like Compute Engine, but that means you still need to maintain the VMs. Even if you’re not able to fully modernize an existing app, it would still be great to get some of the benefits of containers and Kubernetes.

What is Migrate for Anthos?

Enter Migrate for Anthos, a fast and easy way to modernize your existing applications with a service that encapsulates them in a container. Moving your physical servers or existing VMs into Kubernetes containers gives you crucial portability and resource utilization benefits without having to rewrite the underlying application.

Since Migrate for Anthos is built for Google Kubernetes Engine (GKE), you also automatically capture the scaling and flexibility benefits of a managed Kubernetes environment in the cloud. Migrate for Anthos recently became generally available.

Converting an application with Migrate for Anthos happens in two phases. First, it creates a generic container wrapper around your application that makes it seem like it is still running in a full VM environment. Then, you launch Migrate for Anthos software on your Kubernetes cluster that runs the containerized application.

You can find more details about this in the documentation and in our blog post: Migrating from Compute Engine to Kubernetes Engine with Migrate for Anthos.

As the name suggests, Migrate for Anthos works with Anthos GKE. However, you can also use Migrate for Anthos with only GKE—all it requires is your application and a GKE cluster running the Migrate for Anthos software. 

Getting started with Migrate for Anthos

Migrate for Anthos works with a variety of workloads, but not all. It’s particularly adept at migrating legacy applications, stand-alone applications, and monolithic applications. As you start the modernization process, here are some questions to ask to determine whether to use Migrate for Anthos with your applications:

1. Should this app be in the cloud?
By its nature, the cloud may not be able to support some characteristics of your on-prem environment, such as geography and legal compliance. The best way to find out whether the cloud will work for each of your applications is to plan out a full migration. That will allow you to identify groups of applications that can benefit from cloud offerings such as a global network and ease of resizing resources. After that, try out a proof of concept by testing the apps in the cloud to see if it fits your business needs.

2. Should this app be in Kubernetes?
Containerizing an application simplifies workload administration, improves scalability (both up and down), and increases host utilization. Kubernetes orchestrates the containers and GKE handles node upgrades, while add-ons like Istio let you manage network and security policies independently of your application.

With those advantages, it’s easy to think that containers are always the right way to go, but there are some cases where it may make sense to stick with VMs. Strict hardware requirements, specialized kernel modules, and license constraints may be harder to run with containers, negating their advantages.

3. Should this app migration use Migrate for Anthos?
Migrating your apps or workloads to the cloud isn’t just about shifting where the compute resources run; it’s also an opportunity to modernize them with containers. Using Migrate for Anthos (or Migrate for Compute Engine) gives you the ability to get your workloads in the cloud quickly, with minimal upfront downtime that’s easy to plan for. 

However, even if you use the Migrate for Anthos wrapper, your application is still the same application. The benefits of the modern platform may not outweigh a legacy application and a rewrite may be the only way to meet your business needs. There are also some specific services from your VM that may not work with Migrate for Anthos, for example licensing requirements.

Migrate for Anthos can also be the first step in a larger migration effort. Once you’ve migrated the application to GKE, you can gradually break up a monolithic app into microservices by manually rewriting parts. Spreading out the migration effort gets you in the cloud sooner, giving you more time to modernize.

Next steps

A successful modernization starts with creating a full migration plan, testing the workloads, and monitoring them. You can experience the benefits of modernization with Migrate for Anthos by picking a small workload and trying it out for yourself!

Generative AI continues to develop at a blistering pace, making it more important than ever that organizations have access to enterprise-ready capabilities to help them leverage this disruptive technology. 

Harnessing the power of decades of Google’s research, innovation, and investment in AI, Google Cloud continues to make generative AI available with baked-in security, data governance, and scalability across the board. 

To this end, last month, we announced the general availability of Generative AI support on Vertex AI, giving our customers the ability to access powerful foundation models from Google Research and tools for customizing and applying them. 

Today we are announcing the general availability (GA) of four important foundation models for Vertex AI. These include Imagen, PaLM 2 for Chat, Codey, and Chirp. For each of these models, organizations can access APIs on Model Garden and do prompt design and tuning on Generative AI Studio.

• Imagen includes four key features:
  • Image generation for creating studio-grade images at scale
  • Image editing to edit generated or existing images via text prompts 
  • Image captioning for creating captions of images at scale
  • Visual Question & Answering (VQA) for interacting with, analyzing, and explaining images
• PaLM 2 for Chat follows the general availability of PaLM 2 for Text in June 
• Codey supports code generation, completion, and code chat
• Chirp supports multilingual Speech AI 

We’re also announcing Multimodal Embeddings API in preview, which lets customers combine the power of Vertex AI’s generative AI models with their proprietary data, to generate embeddings, or interchangeable vector representations, of their text and image data. These capabilities can enable data science teams to deliver a variety of downstream tasks such as image classification, content recommendations, and visual search. 

In this blog post, we’ll explore what your organization can do with these powerful models and how Vertex AI provides the enterprise-ready capabilities you can use to get up and running with generative AI. 

Helping to drive enterprise value from Generative AI models

Powerful models are the foundation of generative AI, but the software, tools, and infrastructure that surround these models are equally important for enterprise adoption. Organizations face challenges not only accessing these models, but also integrating AI while maintaining protection over intellectual property, adhering to regulations around data security and privacy, and ensuring models and applications are safe to use. Many organizations also want to use generative AI without incurring large costs or managing huge clusters.

We help address these challenges head-on with Vertex AI’s platform capabilities for scalable application integration, purpose-built AI infrastructure, secure and private data customization, and responsible use of this technology. 

Let’s see how each of these pillars can help your organization. 

Access models to build production-ready generative applications 
Vertex AI can make it easy to access foundation models, as today’s model announcements attest. While models are an inextricable part of generative AI, the software that helps enterprises use this technology is equally important—which is why Vertex AI also offers a range of tools for tuning, deploying, monitoring, and maintaining models, so you can build differentiated applications using your own data. 

Turning to today’s announcements, in May we announced Imagen, our foundation model for image generation. Now, we are excited to announce Imagen is generally available with an allowlist (i.e., approved access via your sales representative), letting onboarded customers start using image generation and editing capabilities. Visual Q&A and Captioning for production workloads are also generally available for all customers. Visual Q&A provides new ways to engage with image-based data like retail products or image libraries. This new capability can give you answers to questions about an image, helping you analyze large amounts of data quickly, and it can even help the visually impaired understand images or graphs that they wouldn’t be able to otherwise. Captioning, meanwhile, can make it easy to generate relevant descriptions for your images. Captions can help with indexing and searching, as well as assigning image descriptions to product listings on eCommerce websites. 

“Imagen is beginning to power key capabilities within Omni, Omnicom’s open operating system, that will enable 17,000+ trained and certified users to create audience-driven customized images in minutes. Imagen has been instrumental in offering a scalable platform for image generation and customization. Integrating it into our platform allows us to expand the scope of audience-powered creative inspiration, at a scale that wasn’t previously possible,” said Art Schram, Annalect Chief Product Officer at Omnicom. “We’re starting to adopt the latest features like styles and fine tuning, and engineering data-driven prompts. We look forward to continuing to provide our users relevant visual inspiration in a responsible way.”

“The latest improvements in Imagen’s product preservation capabilities are a perfect match for Typeface’s focus on personalized AI for brands,” explained Vishal Sood, Head of Product at Typeface. “By combining Google Vertex AI’s Imagen with Typeface’s brand-personalized AI, we are able to help enterprises to create 10x personalized content in a fraction of time.”

Google Shopping recently built an application called Product Studio using Imagen on Vertex AI. Product Studio can enable merchants to create rich product images quickly and easily, at a fraction of the time it takes to do professional product photo shoots. “We’re excited about the feedback we’re getting from merchants in our early pilots, who say that Product Studio, which leverages Imagen on Vertex AI, helps them generate and publish lifestyle product photos directly to their product catalogs,” says Jeff Harrell, Google’s Senior Director of Product Management for Merchant Shopping. 

Announced in May, PaLM 2 is a family of models that power dozens of Google products, including Bard and Duet AI in Google Cloud. With the PaLM 2 for Chat model, now generally available, you can leverage Google’s PaLM’s variety of abilities for multi-turn chat applications, such as shopping assistants, customer support agents, and more. 

ThoughtSpot, provider of a widely-adopted business intelligence platform, is using PaLM 2 to build a new feature in ThoughtSpot for Google Sheets called “AI Explain,” which can instantly generate explanations of charts, visuals, and anomalies, and will launch new conversational AI and ML-enabled predictive forecasting capabilities into its analytics platform.

With Codey, your organization’s developers can accelerate a wide variety of coding tasks, helping to empower them to work efficiently and close skills gaps. The model enables not only code completion and code generation capabilities, but also chat to help with debugging, documentation, learning new concepts, and more. Since launching in preview in May, we’ve added additional programming languages including Go, Google Standard SQL, Java, Javascript, Python, and Typescript. We’ve also improved the quality of code responses and increased serving capacity, enabling your developers with the right tools to enter the era of generative engineering.  

“Security and privacy are key to incorporating AI into the software development lifecycle,” said David DeSanto, Chief Product Officer at GitLab. “GitLab leverages Vertex AI to deliver new, AI-powered features with a privacy-first approach, including the ability to run our own models and leverage Codey foundation models built on top of PaLM 2. The GitLab DevSecOps platform empowers organizations to harness the benefits of AI for faster software delivery, while ensuring their data, intellectual property, and source code are protected.”

Originally released in May in preview, Chirp is a version of our 2 billion-parameter speech model, which was trained on millions of hours of audio and supports over 100 languages. Chirp achieves 98% accuracy on English and relative improvement of up to 300% in languages with less than 10 million speakers. Whether the use case involves customer support, transcriptions, or voice control, Chirp can help your organization communicate with customers and constituents inclusively, by engaging audiences in their native languages. 

Last but not least, our Multimodal Embeddings API, now in preview, can unlock an array of new applications, such as image and text-based recommendations, by enabling the processing of text and images interchangeably. This capability complements our Text Embeddings API, which became generally available in June, and remains a recommended choice for those with fully text-based use cases. Multimodal Embeddings API makes it possible to categorize images and text together and can be crucial for use cases like retail recommendation systems that can provide relevant outputs from both images of products and text descriptions.

Match generative AI with infrastructure 
Beyond access to models and tools for building generative AI apps, you need infrastructure to make sure your apps can scale and reliably perform — ideally without running into daunting compute costs or management overhead that distracts your technical talent from building innovative products. Google Cloud offers the choice and power to run smaller models running finite tasks at the lowest latency levels, as well as to run large models capable of cutting-edge experiments. 

As our large language model customers are looking to scale up their projects and applications using our models, they often need assurances that their requests will be serviced with acceptable performance. This is especially critical for delivering real-time applications where customer service is paramount. Starting in August, Vertex AI will support provisioned, dedicated generative AI capacity that can deliver guaranteed throughput. This feature can be especially beneficial to customers who have a high volume of sustained workloads.  

Leverage generative AI while protecting data and privacy 
One capability enabled by Google Cloud is the ability to customize models using your own data. Vertex AI can help customers keep their data protected, secure, and private. When a company tunes a foundation model in Vertex AI, private data, model outputs, and prompts can be kept private, and they are never used in the foundation model training corpus. We recently published a whitepaper, “Adaptation of Large Foundation Models,” which outlines how we help protect customer data. 

Auditability and compliance are essential to helping ensure the security and privacy of customer data. We also engage in comprehensive GDPR privacy efforts, including our transparency commitments for customer data usage and the support for our customer’s Data Protection Impact Assessments (DPIAs). Now, we’re excited to support HIPAA compliance for many of our generally available models on Vertex AI, so that healthcare and life science customers with whom we have a Business Associate Agreement can run workloads with Protected Health Information (PHI) data on Google Cloud. 

Innovate responsibly 
Our AI Principles put beneficial use, user safety, and avoidance of harms above business outcomes and are embedded in how we develop our AI products. We’ve conducted extensive reviews on our generative AI products to identify potential risks and have developed guardrails to mitigate these impacts. For example, to address concerns around safety, we’ve implemented safety filters for bias, toxicity, and other harmful content. We also equip our customers with the tools they need to help reduce risk within their applications and provide recommendations to help navigate responsible AI. 

Bring the power of generative AI to your organization

With both a wide selection of foundation models and extensive, enterprise-grade platform capabilities, Vertex AI continues to unlock ways for your business or organization to access foundation models, tune them on your proprietary data, and leverage them for differentiated apps and digital experiences. To take the next step, visit our product page or reach out to our sales representatives to gain access to our latest capabilities.

The benefits of APIs are becoming more clear in an ever-evolving tech landscape, yet ITDMs still struggle to convince executives and investors to buy into an API-first strategy. Here’s a look at the importance of APIs in a changing world, and how ITDMs can make the business case in order to secure the best API strategy for their organization.

According to Google Cloud’s new “State of the API Economy 2021” report, a majority of IT decision-makers view application programming interfaces, or APIs, as essential ingredients in improved customers experiences, expanded partner engagement, accelerated innovation, and other demands of today’s business environment. This is encouraging: APIs are how software talks to other software, and since much of digital transformation involves combining disparate data and functionality into rich user experiences and process automations, APIs are an essential ingredient in modern business strategies. 

What’s less encouraging: the research surveys primarily IT professionals, not business leaders. It’s clear that IT people see the benefits of APIs in the ever-changing tech landscape, but we still hear regular concerns from these same people that they have trouble convincing executives and investors to buy into an API-first strategy. In this article, we’ll look into why they are having these difficulties and some proven ways to successfully position an API strategy not just as a technological solution, but also as a business requirement.

The importance of APIs in a changing world

The rise of APIs has been heavily influenced by the introduction of disruptive new business models and evolving customer preferences that traditional technologies are not positioned to quickly and efficiently address. 

For example, traditionally, if your business sold tickets to events, it would build physical ticket booths and maybe a website or first-party mobile app. Today, tickets in many cases aren’t so much a physical thing presented to an usher as a digital code that an usher scans. Likewise, tickets are less-often purchased in person as opposed to online, and reliance on a first-party website can be unnecessarily restrictive. It places the burden on the business to attract customers, whereas surfacing organically in social media, search engine results, and other digital experiences lets the business meet customers where they’re already assembled. 

Moreover, as COVID-19 continues to disrupt events throughout the world, many ticket sellers—and most organizations, for that matter—have pivoted to digital-first business interactions as a matter of necessity. All of these changes in the business model, and all of the interacting systems and functionality that underpin them, rely on communication among APIs. 

Similarly, today’s banks cannot grow by simply building more branches or hiring more tellers. Instead, they need to make financial information and functionality available when and where customers require it, whether that means via an ATM, a first-party app, or within some other digital experience. Many banks also need to do more than just present this functionality, as customers are increasingly interested in the analytics and insights their spending patterns can yield. Again, all of these interactions—from customers making a purchase within an app to banks applying machine learning in order to offer customers financial insights—are enabled by APIs.     

Related: The “State of API Economy 2021” report describes how digital transformation initiatives evolved throughout 2020, as well as where they’re headed in the years to come. Download for free.

When guidance meets resistance

These examples do not illustrate technology that updates the status quo, but rather technology that unlocks business opportunities that transcend the status quo—and that help businesses to thrive even as the status quo fades into irrelevance and obsolescence. APIs are thus not just an IT topic but also important business enablers that should be understood by everyone involved with the enterprise’s investments, from internal stakeholders approving business strategies to external shareholders trying to assess an organization’s trajectory. 

The challenge for investor relations is to convey these financial and operational benefits in a way that clearly communicates the need for a new business model rather than refinements to the existing models. It’s essential that IT professionals understand APIs, but it’s also essential for business leaders to understand them too.

This is even trickier given that arguments for API investments are often based on future potential, while arguments for more conservative alternatives are based on past success. 

At a high level, the API value proposition is clear: In the past, valuable functionality and data have been encased in systems and applications, making them difficult to scale or leverage for new, evolving use cases. In contrast, APIs make functionality and data infinitely reusable, infinitely scalable, and modular such that APIs can easily be combined for new uses. All of this accrues to richer user experiences and more flexibility than ever for companies to monetize their digital assets, share them with partners, or combine them with assets from third parties. 

It’s essential that IT professionals understand APIs, but it’s also essential for business leaders to understand them too.

But investors typically want as much information as possible because their decisions can affect not just productivity and output, but company stock prices and potential future growth. High-level arguments may not be persuasive. The deeper assurances investors crave would normally come from guidance.

Guidance in this context refers to insights based on growth forecasts and customer adoption, but this can be difficult early in market entry. Robust forecasting processes need to be developed to demonstrate the efficacy and value of the API economy, which can be hard to predict: whereas APIs are well understood in some sectors, and especially among digital natives, they are in the early stages of the growth rate in other verticals, making it challenging to forecast developer adoption of a given API. And since there is a shortage of information, trying to use traditional guidance comes with a risk of being wrong and thus of little value to investors.

Related: Set your 2021 API resolutions with these top 2020 posts.

How to deliver a more useful value proposition

While guidance may be premature during the early stages of market entry, investor relations teams still need to convey the full value of an enterprise to investors. To do this, they need a value proposition that emphasizes the intrinsic value of the investment while reinforcing the benefits that can best drive business and stock growth. Considering how large an investment of time, effort, and money transitioning to an API economy can be, it is vital to convey that the benefits are substantial.

A solid value proposition should demonstrate maximum returns, and while this shouldn’t include far-fetched or unobtainable claims, it can include reasonable aspirational visions alongside statistical insights. To craft these aspirational narratives, investor relations teams should look to their organization’s existing business needs and challenges, and then demonstrate how APIs can benefit the organization in these areas. Here are some options that speak to a number of common business requirements:

• Sales channel: API investments are reusable, improve speed to market, enable automated processes and partner onboarding, and can uncover unanticipated opportunities.
• Cost: Businesses can reduce operational costs by using and reusing APIs for innovation and business development, and by using the services native to your partner’s digital surface, you can further reduce innovation costs and risks.
• Earnings: API-enabled digital ecosystems unlock a variety of partner services that leverage the business’s shared data to drive new customer acquisition, new market positions, new transaction volumes, and direct API monetization.
• Risk mitigation: By investing in a credible API, businesses can mitigate downside risks that traditional enterprises can face from market disruptors, industry-wide shifts to digital tools, and inabilities to ingest and analyze growing data sources.
• Intellectual property: Unlike project-driven innovation and customized, point-to-point integration that traps enterprise knowledge in small teams and divisional silos, APIs are reusable and modular, breaking down silos and encouraging intra-organizational collaboration.
• Speed to market: The efficient, repeatable API interface informs improvements to the fulfillment process with consistent access to data from across the organization, which drives solutions that more quickly and efficiently meet customer needs.
• Ethics: APIs offer the flexibility and economical advantages that give organizations the capacity to focus on their brand’s ethical “reason for being” beyond profitability by serving economically marginal and underserved market segments.
• Customer credibility: Organizations can deliver the extended, connected digital experiences that customers expect with the tools and flexibility included with API products.
• Employee retention: Businesses can avoid losing key employees by updating their legacy technologies with APIs, giving employees the opportunity to enhance their skills with modern technologies.
• Corporate strategy: Enterprises that use APIs’ reusable, modular structure and tools are more capable of adapting to rapid structural shifts in customer demand patterns and sectoral changes in the economy.

Whichever of these business challenges a team speaks to, it is imperative that they demonstrate the benefits of APIs, and that once they’ve determined the angle they intend to use, they keep their message consistent. While we’ve seen a number of viable ways to position APIs as a winning strategy, switching among them could make the presentation—and APIs in general—seem insubstantial and unreliable. 

This is why it’s key to decide on the most relevant business concerns, and once you’ve tailored your presentation, to make sure that you have message alignment, including buy-in and support from C-level executives. With a strong pitch built around solving existing business concerns and solidarity from relevant stakeholders, you can go into your investor meeting with the confidence to secure the best API strategy for your organization.

Strengthen your pitch with additional insights. Here are five key trends in 2021 for API-first digital transformation.

About the Author: Paul Rohan is a researcher on Open Banking and a Google Cloud solutions consultant. Paul works with banking C-Suites that are examining the impact of the Platform Economy and Digital Ecosystems on financial services industry growth, market structures and governance. Paul is the author of “PSD2 in Plain English” and “Open Banking Strategy Formation”.

Do you need to develop, run and secure applications across your hybrid and multicloud environments? Look no further than Anthos, our managed application platform that extends Google Cloud services and engineering practices to your environments so you can modernize apps faster and establish operational consistency across them. 

To help you get started, we created the Anthos 101 video learning series. It’s a great starting point for understanding the basics of Anthos—and you can watch the whole series in less than an hour.

Let’s dive in.

1. What is Anthos?

Discover what Anthos is and how it helps enterprises manage their applications. You’ll learn about the different tools Anthos offers—like the ability to create environs and platform administrators—to help you modernize and manage your application infrastructure.

https://youtube.com/watch?v=Qtwt7QcW4J8%3Fenablejsapi%3D1%26

2. How to get started with Anthos on Google Cloud

Ready to get started with Anthos? In this lesson, you’ll create your own Anthos deployment. You’ll learn about the different tools on the Anthos dashboard—like the Service Mesh card and Cluster Status cards—plus how to deploy and alter Google Kubernetes Engine (GKE) clusters and Anthos Service mesh via Google Compute Engine.

https://youtube.com/watch?v=ghFiaz7juoA%3Fenablejsapi%3D1%26

3. How to modernize and run Windows apps in Anthos

Running a Windows application that’s in need of modernization? In this lesson, you’ll discover how you can create and deploy a Windows-based application on Anthos, allowing you to modernize existing workloads and manage your application seamlessly. You’ll even learn to do this without requiring access to source code, re-writing, or re-architecting your existing application.

https://youtube.com/watch?v=w6tzIjZhTIk%3Fenablejsapi%3D1%26

4. How to build modern CI/CD with Anthos

Continuous integration? Continuous delivery? These are two things that developers need to think about with container adoption for hybrid or multicloud environments. Learn how Anthos helps you increase your development velocity without compromising the security of your application.

https://youtube.com/watch?v=ayRz5NmM6pI%3Fenablejsapi%3D1%26

5. How to adopt a multi-cluster strategy for your applications in Anthos

There are a number of use cases that might require a multi-cluster strategy, such as maintaining multiple clusters on the cloud and in your own data center. In this lesson, learn the different tools that Anthos offers—such as GKE, Anthos Config Management, and Anthos Service Mesh—to help deploy and manage multiple clusters.

https://youtube.com/watch?v=ZhF-rTXq-Us%3Fenablejsapi%3D1%26

6. How to improve observability using golden signals in Anthos

Observability is important in application development, but without the right tools monitoring your services can be time consuming. In this episode, learn more how Anthos Service Mesh can help you monitor and manage the four Golden Signals—latency, traffic, errors, and saturation—for your application.

https://youtube.com/watch?v=EDcy3KwV22o%3Fenablejsapi%3D1%26

7. How to modernize legacy Java apps with Anthos

Looking to modernize legacy Java applications? In this lesson, you’ll learn the three categories of Java applications and their unique paths for modernization via Anthos. This can help you reduce your dependency on high-cost proprietary software, decrease operational overhead, and increase software delivery speed.

https://youtube.com/watch?v=hQWcx9iyF7E%3Fenablejsapi%3D1%26

8. How to apply a zero trust model for your deployments using Anthos

It’s time to rethink traditional security models when it comes to network observability and consistency for IAM permissions. In this lesson, learn how you can adopt a zero trust posture with Anthos. This allows you to better secure your network, detect underlying network compromises, and ensure workloads are secure before deployment.

https://youtube.com/watch?v=_qG2vazlozY%3Fenablejsapi%3D1%26

9. How to go beyond business continuity with Anthos

Sometimes a business continuity plan that only covers traditional backup and disaster recovery methods simply isn’t enough. In this lesson, learn how Anthos helps resolve issues like data redundancy, scaling without code changes, implementing measurable SLOs, and much more. You’ll also discover how Anthos can help you manage your application beyond the confines of traditional backup and disaster recovery approaches.

https://youtube.com/watch?v=kUxqdjbgcXs%3Fenablejsapi%3D1%26

10. How to simplify identity with Anthos

Managing identities across hybrid and multicloud environments can be troublesome and hard to keep track of. Luckily, Anthos is capable of simplifying identity management for users and workloads. In this lesson, you’ll learn how Anthos can extend and enable existing capabilities, while allowing you to manage IAM permissions across multiple Anthos and GKE environments.

https://youtube.com/watch?v=6P-4ZEwZqZQ%3Fenablejsapi%3D1%26

11. How to optimize costs with Anthos

Learn how you can optimize costs with Anthos through greater observability, improving existing operations, and many other practices.

https://youtube.com/watch?v=8mGICSTRoYw%3Fenablejsapi%3D1%26

Keep learning

This is just a starting point for learning about Anthos. To deepen your knowledge, check out our free on-demand training: Getting started with Anthos. Or, you can download our Anthos Under the Hood ebook, or get hands-on right now with the Anthos sandbox.