Anthos Makes Hybrid and Multi-Cloud Deployments Easy
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Most enterprises have applications in disparate locations—in their own data centers, in multiple public clouds, and at the edge. These apps run on different proprietary technology stacks, which reduces developer velocity, wastes computing resources, and hinders scalability. How can you consistently secure and operate existing apps, while developing and deploying new apps across hybrid and multicloud environments? How can you get centralized visibility and management of the resources? Well, that is why Anthos exists!
This post explores why traditional hybrid and multicloud deployments are difficult, and then shows how Anthos makes it easy to manage applications across multiple environments.
Why is traditional hybrid and multicloud difficult?
In hybrid and multicloud environments, you need to manage infrastructure. Let’s say you use containers on the clouds, and you develop apps using services on Google Cloud and AWS. Regardless of environment, you will need policy enforcement across your IT footprint. To manage your apps across the environment, you need monitoring and logging systems. You need to integrate that data into meaningful categories, like business data, operational data, and alerts.
Digging further, you might use operational data and alerts to inform optimizations, implement automations, and set policies or SLOs. You might use business data to do all those things, and to deploy third-party apps. Then, to actually enact the changes you decide to implement, you need to act on different parts of the system. That means digging into each tool for policy enforcement, securing services, orchestrating containers, and managing infrastructure. Don’t forget, all of this work is in addition to what it takes to develop and deploy your own apps.
Now, consider repeating this set of tasks across a hybrid and multicloud landscape. It becomes very complex, very quickly. Your platform admins, SREs, and DevOps teams who are responsible for security and efficiency have to do manual, cluster-by-cluster management, data collection, and information synthesis. With this complexity, it’s hard to stay current, to understand business implications, and to ensure compliance (not to mention the difficulty of onboarding a new hire). Anthos helps solve these challenges!
How does Anthos make hybrid and multicloud easy?
With Anthos, you get a consistent way to manage your infrastructure, with similar infrastructure management, container management, service management, and policy enforcement across your landscape.
As a result, you have observability across all your platforms in one place, including access to business information, alerts, and operations information. With this information you might decide to optimize, automate, and set policies or SLOs.
Digging deeper into Anthos
Environs
You may have different regions that need different policies, and also have different development, staging, or production environments that need different permissions. Some parts of your work may need more security. That’s where environs come in! Environs are a way to create logical sets of underlying Kubernetes clusters, regardless of which platform those clusters live on.
By considering, grouping, and managing sets of clusters as logical environs, you can think about and work with your applications at the right level of detail for what you need to do, be it acquiring business insights over the entire system, updating settings for a dev environment, or troubleshooting data for a specific cluster. Using environs, each part of the functional stack can take declarative direction about configuration, compliance, and more.
Modernize application development
Anthos also helps modernize application development because it uses environs to enforce policies and processes, and abstracts away the cluster and container management from application teams. Anthos enables you to easily abstract away infrastructure from application teams, making it easy for them to incorporate a wide variety of CI/CD solutions on top of environs. It lets you view and manage your applications at the right level of detail, be it business insights for services across the entire system, or troubleshooting data for a specific cluster. Anthos also works with container-based tools like buildpacks to simplify the packaging process. It offers Migrate for Anthos to take those applications out of the VMs and move them to a more modern hosting environment.
What’s in it for platform administrators?
Anthos provides platform administrators a single place to monitor and manage their landscape, with policy control and marketplace access. This reduces person-hours needed for management, enforcement, discovery, and communication. Anthos also provides administrators an out-of-the-box structured view of their entire system, including services, clusters, and more, so they can improve security, use resources more efficiently, and demonstrate measurable success. Administrators also save time and effort by managing declaratively, and they can communicate the success, cost savings, and efficiency of the platforms without needing to manually combine data.
Interested in getting started with Anthos? Check out the free on-demand training here and my YouTube series.
For more resources, you can also read the Anthos ebook at no cost. For more #GCPSketchnote and similar cloud content, follow me on twitter @pvergadia and keep an eye out on thecloudgirl.dev
Can Your Company Use Video AI? You’d Be Surprised at the Answer
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Video AI is a powerful way to enable content discovery and engaging video experiences.
Here, try it out right now!
Google Cloud’s easy-to-access video AI solutions can accomplish a bunch of things. Here are a few:
Precise video analysis: Video Intelligence API automatically recognizes more than 20,000 objects, places, and actions in stored and streaming video. It also distinguishes scene changes and extracts rich metadata at the video, shot, or frame level. Use in combination with AutoML Video Intelligence to create your own custom entity labels to categorize content. Imagine being able to categorise hundreds of videos of customer interactions quickly to improve service training!
Recommended content: Build a content recommendation engine with labels generated by Video Intelligence API and a user’s viewing history and preferences. This will simplify content discovery for your users and guide them to the most relevant content that they want.
Simplify media management: Find value in vast archives by making media easily searchable and discoverable. Easily search your video catalog the same way you search text documents. Extract metadata that can be used to index, organize, and search your video content, as well as control and filter content for what’s most relevant.Imagine being able to locate insight in hundreds of enterprise videos to improve productivity and customer experience!
Easily create intelligent video apps: Gain insights from video in near real time using the Video Intelligence Streaming Video APIs, and trigger events based on objects detected. Build engaging customer experiences with highlight reels, recommendations, interactive videos, and more. Marketers, imagine being able to trigger a customer workflow, in real time, based on a live customer interactions.
Automate expensive workflows: Reduce time and costs associated with transcribing videos and generating closed captions, as well as flagging and filtering inappropriate content.
Content moderation: Identify when inappropriate content is being shown in a given video. You can instantly conduct content moderation across petabytes of data and more quickly and efficiently filter your content or user-generated content.
What can your organisation do with video AI?
How The New York Times Increased Speed of Delivery by Using Kubernetes
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When New York Times decided a few years ago to move out of its data centers, its first deployments on the public cloud were smaller and less critical applications that were being managed on virtual machines.
“We started building more and more tools, and at some point, we realized that we were doing a disservice by treating Amazon as another data center,” says Deep Kapadia, Executive Director, Engineering at The New York Times.
Kapadia was tapped to lead a Delivery Engineering Team that would “design for the abstractions that cloud providers offer us.”
The team decided to use Google Cloud Platform and its Kubernetes-as-a-service offering, GKE (Google Kubernetes Engine). Owing to Google Cloud solution and GKE, The New York Times was able to increase the speed of delivery.
Some of the legacy VM-based deployments took 45 minutes; with Kubernetes, that time was “just a few seconds to a couple of minutes,” says Brian Balser, Engineering Manager at The New York Times.
“Teams that used to deploy on weekly schedules or had to coordinate schedules with the infrastructure team, now deploy their updates independently, and can do it daily when necessary,” says Tony Li, Site Reliability Engineer, The New York Times.
Adopting Cloud Native Computing Foundation technologies allowed The New York Times to have a more unified approach to deployment across the engineering staff, and portability for the company.
Plainsight Vision AI Available for Google Cloud Customers to Unlock Accurate, Actionable Insights
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Data-savvy businesses increasingly rely on images and videos for critical functions, and yet are challenged by the sheer mass of information—more than 3.2 billion images and 720,000 hours of video are created daily. This explosion in visual data has paved the way for the growth of computer vision, a form of artificial intelligence (AI) that enables computers to “see” the world similarly to the way people do, but with unblinking consistency, and greater accuracy.
The transformational impact and value of computer vision solutions are significant and has been a guiding objective for companies and AI developers. And yet, even as the applications for computer vision increase dramatically, architecting and implementing vision AI solutions remain highly complex. Visual data, such as images and video, are made up of thousands of pixels of information that represent millions of different patterns and meanings, which can make interpreting even a single image overwhelming from a computational perspective.
Many organizations struggle with deployments and fail to operationalize vision AI solutions due to development delays, machine learning and data science hiring challenges, inaccurate output, a lack of integration with existing infrastructure, difficulty of use, and high cost. Plainsight, with the power of Google Cloud resources, is addressing all these challenges and helping businesses by enabling the deployment of vision AI within enterprise private networks that can be managed easily and scaled economically.
Plainsight has announced availability of its vision AI platform on Google Cloud Marketplace. Businesses can now easily deploy end-to-end vision AI to private clouds to realize the full value of their video and other visual data for accurate, actionable insights across diverse use cases.
Delivering on the Promise of AI: Seeing What’s Hiding In Plain Sight
For organizations to integrate AI and machine learning into their businesses successfully, the technology must be powerful enough to solve real challenges, yet fast, easy, and accessible enough to ensure the innovation potential is realized. Plainsight on Google Cloud delivers the power of enterprise vision AI that’s quick and easy to use with Google Cloud resources that enable global scale, increased security, bolstered privacy, unified billing, and cost savings.
To streamline vision AI workflows, Plainsight facilitates the entire pipeline, from visual data ingestion and annotation, through continuous model training, deployment, and monitoring for easier innovation and faster time-to-production. Our platform accelerates vision AI development in a manner that is complete, accurate, and accessible to non-technical business leaders. We believe that AI should be available and accessible to anyone and everyone—so that teams across entire organizations can reap the benefits.
By integrating Plainsight into their private networks, companies worldwide can now leverage one intuitive platform for centralized control of streamlined vision AI model creation and training with optimized visual data handling for diverse enterprise solutions. These use cases include: social distancing monitoring, medical imaging, drug compound screening, defect detection in manufacturing processes, identifying gas leaks, or even livestock counting and crop health monitoring for agriculture, to name a few.https://www.youtube.com/embed/A7U_0UkjvEg?enablejsapi=1&
We enable customers so they can create successful solutions that enable them to clearly see their business from all angles and to take advantage of the knowledge visual data can reveal by simply and quickly operationalizing practical vision AI applications.
AI-Powered Dataset Creation, Automated Model Training & Easy Deployment Without A Single Line Of Code
For vision AI applications, success is inextricably dependent on the quality and quantity of the datasets required to train the relevant models. To aid enterprises in this vital stage, the Plainsight platform provides built-in data annotation for the fast and easy creation of datasets. This includes AI-powered features that accelerate the speed and quality of labeling such as SmartPoly, for the automated polygon masking of objects, TrackForward, to predict and automatically label objects from frame to frame in video annotations, and AutoLabel for automated object recognition and labeling based on pre-trained machine learning models, to highlight a few.
In addition, to ensure the success of AI integration, we significantly reduce time-intensive processes with Plainsight vision AI’s automated machine learning with continuous model training and easy deployment capabilities. In just a few clicks, users can leverage optimizations for the most reliable model training without endless experimentation cycles. And, models are easily deployed at scale all within one, easy-to-manage model operationalization process for the business.
Growing With Google Cloud
Plainsight is a vision AI innovation leader, developing solutions that address unmet needs for challenger brands and Fortune 500s across vertical markets. As a team recognized for succeeding where others have failed, our expanding partnership with Google Cloud provides a powerful combination that helps customers see and activate the value of their visual data with a suite of services in a secure and private manner.
Our vision AI Platform simplifies building and operationalizing AI to solve business problems enterprises are facing every day—and the demand is increasing. To accelerate our journey to faster, more accessible AI for enterprises, we knew we needed strong support to grow Plainsight and scale our backend tools to match our vision.
Google Cloud delivered everything, and more, in one program. The Startup Program by Google Cloud provided the technology and services for scale and the support we needed to maximize the value the Program provided us. The Startup Program has been a springboard for architecting Plainsight vision AI in the cloud, accelerating our goals and optimizing innovation, efficiency, and growth. The team also helped us optimize Google Ads campaigns, fueling adoption of Plainsight.
After launching the SaaS version of Plainsight Data Annotation in November 2020, we grew our user base by nearly 110x in just three short months. Google Ads has also dramatically increased website traffic, growing new users by nearly 5.75X and page views by over 5X. The Google team helped us identify where Google Cloud offerings could be leveraged instead of developing in-house solutions and offered best practices that enabled us to deliver faster on our initiatives.
Kubernetes was already the underlying component of our platform and leveraging Google Kubernetes Engine (GKE) as a managed service removed a layer of complexity. By combining GKE and Anthos, we were able to standardize our deployments, aligning to how our customers leverage Anthos for enterprise applications in their own organizations. In addition, as a fast-moving, customer-centric company we use Google Workspace to help us centralize and manage our day-to-day work internally. By leveraging multiple products across Google’s ecosystem, we take advantage of a holistic partnership that has helped our business tremendously as we scale.
Leveraging Google’s Partners for Strategic Consultation
To facilitate this expansion of our partnership with Google and to maximize our use of Google Cloud services, we are working with DoiT International, a Google Managed Services Provider and 2020 Global Reseller Partner of the Year. DoiT provides us with ongoing technical consultation for cloud-native architecture, Google Cloud Marketplace integration, production-grade Kubernetes support, Google Cloud cost optimization, and technical support. The DoiT team has been invaluable in compiling best practices, tips, and strategies from their vast experience with various cloud customers to ease our Marketplace integration and is providing input for infrastructure strategy to support our continued rapid growth.
Plainsight Delivers Enterprise Vision AI Through The Google Cloud Platform Marketplace
Plainsight vision AI is now available to Google Cloud Customers on Google Cloud Marketplace enabling organizations across industries to deploy private Plainsight instances within their own environments. Marketplace customers will benefit from Google Cloud privacy, security, scalability and unified billing through their Google Cloud account.
Combining the powerful benefits provided by Google Cloud resources with Plainsight’s vision AI Platform into private networks, enterprises worldwide can now leverage one intuitive platform for centralized control of streamlined vision AI model creation and training with optimized visual data handling for diverse enterprise solutions.
Through our Google partnership, we’re able to leverage a powerful foundation that allows us to rapidly innovate, scale and accelerate delivery on our vision AI platform capabilities. By executing on our vision to make AI easier, faster and more accessible for all users across entire enterprises, we’re helping businesses see more and by seeing more, they’ll have the power to solve more.
If you want to learn more about how Google Cloud can help your startup, visit our page here where you can apply for our Startup Program, and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.
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The 2019 Accelerate State of DevOps Report confirms analyst reports that DevOps is crossing the chasm with the proportion of elite performers almost tripling compared to last year.
The report finds that delivering software quickly, reliably, and safely is at the heart of technology transformation and organizational performance. The report also finds that there’s a right way to handle the change approval process, and it leads to improvements in speed and stability and reductions in burnout.
Download this report and get:
- Insights on how to achieve elite performance in software development and delivery so you can deliver more value to your customers and stakeholders.
- Role of cloud computing in driving technology and organizational performance, with guidance on how to improve your use of the cloud.
- Smart ways to improve productivity and how the highest performers scale DevOps to maximize success.
All About Cloud Run, its Scalability and Management Features
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Mindful Containers is a fictitious company that is creating containerized microservice applications. They need a fully managed compute environment for deploying and scaling serverless containerized microservices. So, they are considering Cloud Run.
They are excited about Cloud Run because it abstracts away the cluster configuration, monitoring, and management so they can focus on building the features for their apps. Cloud Run is a fully-managed compute environment for deploying and scaling serverless containerized microservices.
What is Cloud Run?
Cloud Run is a fully-managed compute environment for deploying and scaling serverless HTTP containers without worrying about provisioning machines, configuring clusters, or autoscaling.
- No vendor lock-in – Because Cloud Run takes standard OCI containers and implements the standard Knative Serving API, you can easily port over your applications to on-premises or any other cloud environment.
- Fast autoscaling – Microservices deployed in Cloud Run scale automatically based on the number of incoming requests, without you having to configure or manage a full-fledged Kubernetes cluster. Cloud Run scales to zero— that is, uses no resources—if there are no requests.
- Split traffic – Cloud Run enables you to split traffic between multiple revisions, so you can perform gradual rollouts such as canary deployments or blue/green deployments.
- Custom domains – You can set up custom domain mapping in Cloud Run and it will provision a TLS certificate for your domain.
- Automatic redundancy – Cloud Run offers automatic redundancy so you don’t have to worry about creating multiple instances for high availability
How to use Cloud Run
With Cloud Run, you write your code in your favorite language and/or use a binary library of your choice. Then push it to Cloud Build to create a container build. With a single command—“gcloud run deploy”—you go from a container image to a fully managed web application that runs on a domain with a TLS certificate and auto-scales with requests.
How does Cloud Run work?
Cloud Run service can be invoked in the following ways:
HTTPS: You can send HTTPS requests to trigger a Cloud Run-hosted service. Note that all Cloud Run services have a stable HTTPS URL. Some use cases include:
- Custom RESTful web API
- Private microservice
- HTTP middleware or reverse proxy for your web applications
- Prepackaged web application
gRPC: You can use gRPC to connect Cloud Run services with other services—for example, to provide simple, high-performance communication between internal microservices. gRPC is a good option when you:
- Want to communicate between internal microservices
- Support high data loads (gRPC uses protocol buffers, which are up to seven times faster than REST calls)
- Need only a simple service definition you don’t want to write a full client library
- Use streaming gRPCs in your gRPC server to build more responsive applications and APIs
WebSockets: WebSockets applications are supported on Cloud Run with no additional configuration required. Potential use cases include any application that requires a streaming service, such as a chat application.
Trigger from Pub/Sub: You can use Pub/Sub to push messages to the endpoint of your Cloud Run service, where the messages are subsequently delivered to containers as HTTP requests. Possible use cases include:
- Transforming data after receiving an event upon a file upload to a Cloud Storage bucket
- Processing your Google Cloud operations suite logs with Cloud Run by exporting them to Pub/Sub
- Publishing and processing your own custom events from your Cloud Run services
Running services on a schedule: You can use Cloud Scheduler to securely trigger a Cloud Run service on a schedule. This is similar to using cron jobs. Possible use cases include:
- Performing backups on a regular basis
- Performing recurrent administration tasks, such as regenerating a sitemap or deleting old data, content, configurations, synchronizations, or revisions
- Generating bills or other documents
Executing asynchronous tasks: You can use Cloud Tasks to securely enqueue a task to be asynchronously processed by a Cloud Run service. Typical use cases include:
- Handling requests through unexpected production incidents
- Smoothing traffic spikes by delaying work that is not user-facing
- Reducing user response time by delegating slow background operations, such as database updates or batch processing, to be handled by another service,
- Limiting the call rate to backend services like databases and third-party APIs
Events from Eventrac: You can trigger Cloud Run with events from more than 60 Google Cloud sources. For example:
- Use a Cloud Storage event (via Cloud Audit Logs) to trigger a data processing pipeline
- Use a BigQuery event (via Cloud Audit Logs) to initiate downstream processing in Cloud Run each time a job is completed
How is Cloud Run different from Cloud Functions?
Cloud Run and Cloud Functions are both fully managed services that run on Google Cloud’s serverless infrastructure, auto-scale, and handle HTTP requests or events. They do, however, have some important differences:
- Cloud Functions lets you deploy snippets of code (functions) written in a limited set of programming languages, while Cloud Run lets you deploy container images using the programming language of your choice.
- Cloud Run also supports the use of any tool or system library from your application; Cloud Functions does not let you use custom executables.
- Cloud Run offers a longer request timeout duration of up to 60 minutes, while with Cloud Functions the requests timeout can be set as high as 9 mins.
- Cloud Functions only sends one request at a time to each function instance, while by default Cloud Run is configured to send multiple concurrent requests on each container instance. This is helpful to improve latency and reduce costs if you’re expecting large volumes.
Pricing
Cloud Run comes with a generous free tier and is pay per use, which means you only pay while a request is being handled on your container instance. If it is idle with no traffic, then you don’t pay anything.
Conclusion
After learning about the ease of set up, scalability, and management capabilities of Cloud Run the Mindful Containers team is using it to deploy stateless microservices. If you are interested in learning more, check out the documentation.https://www.youtube.com/embed/oR4btKLRdn4?enablejsapi=1&
For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev
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