Enterprise application teams need to manage more than just containers in their software supply chain. That’s why we created Artifact Registry, a fully-managed service with support for both container images and non-container artifacts.

Artifact Registry improves and extends upon the existing capabilities of Container Registry, such as customer-managed encryption keys, VPC-SC support, Pub/Sub notifications, and more, providing a foundation for major upgrades in security, scalability and control. While Container Registry is still available and will continue to be supported as a Google Enterprise API, going forward new features will only be available in Artifact Registry, and Container Registry will only receive critical security fixes.

Below, we’ll highlight the key improvements Artifact Registry provides over Container Registry, as well as the steps to start using it today.

A unified control plane for container, OS and language repositories

Artifact Registry includes more than just container images: as a developer, you can store multiple artifact formats, including OS packages for Debian and RPM, as well as language packages for popular languages like Python, Java, and Node. In addition, you can manage them all from a single, unified interface. 

A more granular permission model with Cloud IAM

Artifact Registry comes with fine-grained access control via Cloud IAM. Unlike Container Registry, this allows you to control access on a per-repository basis, rather than all images stored in a project. This enables you to scope permissions as granularly as possible, for example to specific regions or environments as necessary.

Repositories in the region of your choice

Artifact Registry supports the creation of regional repositories, which allows you to put your artifacts and data directly in the location that they’ll be used, allowing for higher availability and speed. In Container Registry, you’re limited to “multi-regions”: for example, the closest multi-region for Australia is Asia. However, with Artifact Registry’s regional support, you can create a repository directly in the Sydney data center.

A pricing model that respects your region

While Artifact Registry’s pricing is still based on a combination of network egress and storage usage, support for regional repositories means that you can choose in what region to host your container repositories. Although per unit storage costs are higher for Artifact Registry, optimizing the locations of your repositories to be hosted in the same region where they are used can result in cost savings, because any network traffic within the same region is not considered egress and is thus free.

Part of a secure supply chain

Artifact Registry was designed from the ground up to integrate into our suite of secure supply chain products. This means that it can optionally use Container Analysis to scan your container images for vulnerabilities as they’re uploaded to Artifact Registry, and works directly with Binary Authorization to secure your deployments.

We’re here to help you migrate

If you already use Container Registry, you can take advantage of all the current and upcoming features of container image storage with Artifact Registry by migrating to it. To help, we’ve prepared the following guides:

• Transitioning from Container Registry provides an overview of how to use Artifact Registry instead of Container Registry in a backwards-compatible way
• Copying images from Container Registry guide you to move container images from an existing repository to an Artifact Registry repository

If you’re currently hosting your container images with a third party, you can begin using Artifact Registry directly, by following the instructions in our guide, Migrating containers from a third-party registry, which shows you how to avoid rate limits on image pulls or third-party outages which can disrupt your builds and deployments.

And if you’re just getting started storing container images, you can begin using Artifact Registry as your image repository right away. To learn how, check out Artifact Registry quickstart for Docker, a guide to using Artifact Registry as a single location for managing private packages and Docker container images.

Join our community 

Our Artifact Registry communities are also great resources to help answer your questions and for guidance on best practices: 

• Ask questions on Stack Overflow using the google-artifact-registry tag
• Visit the Google Cloud Slack community and ask a question in the #artifact-registry channel. If you haven’t already joined the Slack community, use this form to sign up.

In 2020, many businesses across industries turned their focus and investments towards digital strategies. APIs being an integral part of every organization’s digital disruption, will grow in relevance throughout 2021. Read the report to gain more insights on driving API-led digital transformations and in-depth analysis of Google Cloud’s Apigee API Management Platform usage data, case studies and third-party surveys conducted with tech leaders.

Vodafone India had a manual, labor-intensive process for determining network capabilities and reach. The company sent field operatives to every customer location to conduct a feasibility study. These feasibility studies help Vodafone determine whether they can provide connectivity and services to customers based on the infrastructure at that location.

Everyone from the IT team and end users to the field operatives doing the work recognized the need to adopt a new solution to automate the measurements. They needed a technology that was easy to use and maintain.

“Vodafone used to manually perform physical surveys for each feasibility, which is a time-consuming and labor-intensive process. Often, feasibility studies were delayed, and we missed out on opportunities to serve additional customers. With SmartFeasibility, we’ve increased our capacity 15 fold, which positively impacts our bottom line and allows us to provide better and smarter customer service.”

—Rajneesh Asthana, IT Planning and Delivery, Vodafone India

Partnering with Lepton Software (a leading global provider of location-based analytics solution) Vodafone introduced SmartFeasibility—a solution that changed the feasibility testing from a manual to an automated process. This involved a full Google geo platform solution – leveraging world class technology like maps, roads and directions.

Google Maps Platform Results

• Employees are able to access information faster with SmartFeasibility—they have data at their fingertips, rather than waiting for an employee to collect it
• Field operatives have increased their conversion rates by providing more accurate readings on feasibilities and closing more customer business
• Addresses are now easy to find with a click of a button. The Vodafone India team can search feasibilities that have been loaded into the database, so if there’s an issue or if they need to reference a past action, they have that information at their fingertips
• 2 day turnaround versus 5 before the solution was implemented
• 400+ new customers added per day

With the new solution, Vodafone India no longer needs field operatives to manually calculate these measurements. With Google Maps, users can search customers’ addresses, calculate the distance between Vodafone’s location and the customer’s location and research building data such as height.

The old system of having field operatives collect data was unreliable. With Google Maps Platform, the Vodafone India team knows that the measurements are accurate and reliable.

Today, more and more applications are being deployed in containers on Kubernetes—so much so that we’ve heard Kubernetes called the Linux of the cloud.

Despite all that growth on the application layer, the data layer hasn’t gotten as much traction with containerization. That’s not surprising, since containerized workloads inherently have to be resilient to restarts, scale-out, virtualization, and other constraints. So handling things like state (the database), availability to other layers of the application, and redundancy for a database can have very specific requirements. That makes it challenging to run a database in a distributed environment. 

However, the data layer is getting more attention, since many developers want to treat data infrastructure the same as application stacks.

Operators want to use the same tools for databases and applications, and get the same benefits as the application layer in the data layer: rapid spin-up and repeatability across environments. In this blog, we’ll explore when and what types of databases can be effectively run on Kubernetes.

Before we dive into the considerations for running a database on Kubernetes, let’s briefly review our options for running databases on Google Cloud Platform (GCP) and what they’re best used for.

• Fully managed databases. This includes Cloud Spanner, Cloud Bigtable and Cloud SQL, among others. This is the low-ops choice, since Google Cloud handles many of the maintenance tasks, like backups, patching and scaling. As a developer or operator, you don’t need to mess with them. You just create a database, build your app, and let Google Cloud scale it for you. This also means you might not have access to the exact version of a database, extension, or the exact flavor of database that you want.
• Do-it-yourself on a VM. This might best be described as the full-ops option, where you take full responsibility for building your database, scaling it, managing reliability, setting up backups, and more. All of that can be a lot of work, but you have all the features and database flavors at your disposal.
• Run it on Kubernetes. Running a database on Kubernetes is closer to the full-ops option, but you do get some benefits in terms of the automation Kubernetes provides to keep the database application running. That said, it is important to remember that pods (the database application containers) are transient, so the likelihood of database application restarts or failovers is higher. Also, some of the more database-specific administrative tasks—backups, scaling, tuning, etc.—are different due to the added abstractions that come with containerization.

Tips for running your database on Kubernetes

When choosing to go down the Kubernetes route, think about what database you will be running, and how well it will work given the trade-offs previously discussed.

Since pods are mortal, the likelihood of failover events is higher than a traditionally hosted or fully managed database. It will be easier to run a database on Kubernetes if it includes concepts like sharding, failover elections and replication built into its DNA (for example, ElasticSearch, Cassandra, or MongoDB). Some open source projects provide custom resources and operators to help with managing the database.

Next, consider the function that database is performing in the context of your application and business. Databases that are storing more transient and caching layers are better fits for Kubernetes. Data layers of that type typically have more resilience built into the applications, making for a better overall experience.  

Finally, be sure you understand the replication modes available in the database. Asynchronous modes of replication leave room for data loss, because transactions might be committed to the primary database but not to the secondary database(s). So, be sure to understand whether you might incur data loss, and how much of that is acceptable in the context of your application.

After evaluating all of those considerations, you’ll end up with a decision tree looking something like this:

How to deploy a database on Kubernetes

Now, let’s dive into more details on how to deploy a database on Kubernetes using StatefulSets.

With a StatefulSet, your data can be stored on persistent volumes, decoupling the database application from the persistent storage, so when a pod (such as the database application) is recreated, all the data is still there.

Additionally, when a pod is recreated in a StatefulSet, it keeps the same name, so you have a consistent endpoint to connect to. Persistent data and consistent naming are two of the largest benefits of StatefulSets. You can check out the Kubernetes documentation for more details.

If you need to run a database that doesn’t perfectly fit the model of a Kubernetes-friendly database (such as MySQL or PostgreSQL), consider using Kubernetes Operators or projects that wrap those database with additional features. Operators will help you spin up those databases and perform database maintenance tasks like backups and replication. For MySQL in particular, take a look at the Oracle MySQL Operator and Crunchy Data for PostgreSQL. 

Operators use custom resources and controllers to expose application-specific operations through the Kubernetes API. For example, to perform a backup using Crunchy Data, simply execute pgo backup [cluster_name]. To add a Postgres replica, use pgo scale cluster [cluster_name].

There are some other projects out there that you might explore, such as Patroni for PostgreSQL. These projects use Operators, but go one step further. They’ve built many tools around their respective databases to aid their operation inside of Kubernetes. They may include additional features like sharding, leader election, and failover functionality needed to successfully deploy MySQL or PostgreSQL in Kubernetes.

While running a database in Kubernetes is gaining traction, it is still far from an exact science. There is a lot of work being done in this area, so keep an eye out as technologies and tools evolve toward making running databases in Kubernetes much more the norm. 

When you’re ready to get started, check out GCP Marketplace for easy-to-deploy SaaS, VM, and containerized database solutions and operators that can be deployed to GCP or Kubernetes clusters anywhere.

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.