Enterprises need to move faster than ever to gain a competitive advantage in today’s customer-focused environment. Time-to-market for products and services has shortened dramatically, from years to days. IT teams must move fast, react fast, and enable business strategies via constant innovation. 

All of this digital transformation is about more than adopting the latest technologies. It is also about maximizing the use of existing data and services to improve efficiency and productivity, drive engagement and growth, and ultimately make the lives of customers, partners, and staff better. Connecting existing data and services and making them easily accessible via APIs promises a path forward, empowering enterprises to extend the value they already possess with new technologies, managed services,  ecosystems, and support.

In addition to the challenges of legacy data and systems, today’s organizations are overwhelmed by the variety of cloud applications to meet their business needs and deliver innovative services to their customers. 

Managing all of this data, connecting sources, integrating applications, and surfacing them as easy-to-use APIs for development is a crucial competency for any IT organization.

Design APIs with an Outside-in Approach

Many IT organizations have focused on solving this challenge with an “inside-out” approach: starting with the integration layer, building the flow, and then developing the APIs. But this approach is inefficient and fundamentally flawed because it looks at the problem from an “exposure” model; rather than designing for the business use cases of developers and other API consumers. Leaders in the organization end up seeing all of this data, connectivity, and integration as the “table stakes plumbing”, and thus do not seek inputs regarding business value from key business stakeholders.

The key issue is not exposure but rather how you leverage your data, services, and systems to drive impact across your digital value chain. Goals such as meeting your adoption or sales targets, reducing costs across lines of business, speeding up time to market, and reducing time spent supporting your customers may all be within reach. Easy-to-use APIs, rather than crudely exposed systems, are foundational enablers of this impact, and they are almost always designed from the outside-in, from the perspective of teams that are consuming the APIs to achieve a business goal.

Embrace API-first Integration

To accelerate the speed of development, enterprise IT teams need to take an API-first approach to integration, starting with the consumers’ use cases rather than the structure of the data in their systems. 

The notion of outside-in thinking should be familiar to product managers, who routinely have to demonstrate customer empathy and put themselves in their customers’ shoes. If your team has a product owner, be sure they are empowered to decide what functionality is needed from their data. 

Maximize your APIs with the right technology enablers

An API-first strategy treats the API not as middleware but as a software product that empowers developers, enables partnerships, and accelerates innovation—a big shift from integration-first operations in which APIs are typically exposed and then forgotten. 

Possessing APIs is only part of the equation. If a company is going to share valuable digital assets with outsiders, it needs API management tools to:

• apply security protections, such as authentication and authorization
• protect assets from malicious attacks
• monitor digital services to ensure availability and high performance
• measure and track usage of the assets 

With the right tools in place, APIs can unlock incredible business opportunities—which is a reason for every enterprise to aspire to be API-first!

Visit our website to learn more about API management with Google Cloud.

Gartner® named Google as a Leader in the 2022 Magic Quadrant™ for Cloud AI Developer Services report. This evaluation covered Google’s language, vision and structured data products including AutoML, all of which we deliver through Google Cloud. We believe this recognition is a reflection of the confidence and satisfaction that customers have in our language, vision, and AutoML products for developers. Google remains a Leader for the third year in a row, based upon the completeness of our vision and our ability to execute.

Developers benefit in many ways by using Cloud AI services and solutions. Customers recognize the advantages of Google’s AI and ML services for developers, such as Vertex AI, BigQuery ML, AutoML and AI APIs. In addition, customers benefit from the pace of progress in the field of Responsible AI and actionable ethics processes applied to all customer and partner solutions leveraging Google Cloud technology, as well as our core architecture including the Vertex AI platform, vision, conversational AI, language and structured data, and optimization services and key vertical industry solutions.

We believe that our ‘Leader’ placement validates this vision for AI developer tools. Let’s take a closer look at some of the report findings.

ML tools purpose-built for developers

Google’s machine learning tools have been built by developers, for developers, based on the groundbreaking research generated from Google Research and DeepMind. This developer empathy drives product development, which supports the developer community to achieve deep value from Google’s AI and ML services. An example of this is the unification of all of the tools needed for building, deploying and managing ML models into one ML platform, Vertex AI, resulting in accelerated time to production. They also cite BigQuery ML, AutoML for language, vision video and tabular data) and prebuilt ML APIs (such as speech and translation) as having high utility for developers at all levels of ML expertise to build custom AI and quickly infuse AI into their applications.

Leading organizations like OTOY, Allen Institute for AI and DeepMind (an Alphabet subsidiary) choose Google for ML, and enterprises like Twitter, Wayfair and The Home Depot shared more about their partnership with Google in their recent sessions at Google Next 2021.

Responsible AI principles and practices

Responsible AI is a critical component of successful AI. A 2020 study commissioned by Google Cloud and the Economic Intelligence Unit highlighted that ethical AI does not only prevent organizations from making egregious mistakes, but that the value of responsible AI practices for competitive edge, as well as talent acquisition and retention are notable. At Google, we not only apply our ethics review process to first party platforms and solutions, to ensure that our services design-in responsible AI from the outset, we also consult with customers and partners based on AI principles to deliver accountability and avoid unfair biases. In addition, our best-in-class tools provide developers with the functionality they need to evaluate fairness and biases in datasets and models. Our Explainable AI tools such as model cards provide model transparency in a structured, accessible way, and the What-If Tool is essential for developers and data scientists to evaluate, debug and improve their ML models.

Clear and understandable product architecture

Google Cloud’s investment in our ML product portfolio has led to a comprehensive, integrated and open offering that spans breadth (across vision, conversational AI, language and structured data, and optimization services) and depth (core AI services, with features such as Vertex AI Pipelines and Vertex Explainable AI built on top). Industry-specific solutions tailored by Google for retail, financial services, manufacturing, media and healthcare customers, such as Recommendations AI, Visual Inspection AI, Media Translation, Healthcare Data Engine, add another layer leveraging this foundational platform to help organizations and users adopt machine learning solutions more easily.

At Google Cloud, we refuse to make developers jump through hoops to derive value out of our technology; instead, we bring the value directly to them by ensuring that all of our AI and ML products and solutions work seamlessly together. To download the full report, click here. Get started on Vertex AI and talk with our sales team.

Disclaimer:

Gartner, Magic Quadrant for Cloud AI Developer Services, Van Baker, Arun Batchu, Erick Brethenoux, Svetlana Sicular, Mike Fang, May 23, 2022.

Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Today, many applications in organizations’ data centers run on Windows Server. Modernizing these traditional Windows apps onto Kubernetes promises a host of benefits: a consistent platform across environments, better portability, scalability, availability, simplified management and speed of deployment, just to name a few. But how? Rewriting traditional .NET applications to run on Linux with .NET Core can be challenging and time-consuming. There is, however, a lower-toil, more developer friendly option.

Last year, we announced support for Windows Server containers running on Google Kubernetes Engine (GKE), our cloud-based managed Kubernetes service, which lets you take the advantage of containers without porting your apps to .NET core or rewriting them for Linux. Today, we’re going a step further with support for Windows Server containers on Anthos clusters on VMware in your on-premises environment. Now available in preview, you can consolidate all your Windows operations across on-prem and Google Cloud.

Bringing Windows Server support to our family of Kubernetes-based services—GKE running on Google Cloud, and Anthos everywhere—with the same experience, lets you modernize apps faster and achieve a consistent development and deployment experience across hybrid and cloud environments. Further, by running Windows and Linux workloads side by side, you get operational consistency and efficiency—no need to have multiple teams specializing in different tooling or platforms to manage different workloads. The single-pane-of-glass view and the ability to manage policies from a central control plane simplifies the management experience, while bin packing multiple Windows applications drives better resource utilization, leading to infrastructure and license savings.

With all these benefits, it’s no surprise that customers such as Thales, a French multinational firm specializing in aerospace and security services, have been able to reap significant benefits by moving Windows applications to GKE. 

“We moved our Windows applications from VMs to Windows containers on GKE and now have a unified mechanism for Linux and Windows-based application management, scaling, logging, and monitoring. Earlier, setting up these applications in VMs and configuring them for high availability used to take up to a week, and the applications were not easily scalable,” said Najam Siddiqui, Solutions Architect at Thales. “Now with GKE, the setup takes only a few minutes. GKE’s automatic scaling and built-in resiliency features make scaling and high-availability setup seamless. Also, manually maintaining the VMs and applying security patches used to be tedious, which is now handled by GKE.” 

Let’s take a deeper look at the architecture that lets you run your Windows container-based workloads on-prem. 

Windows Server running on-prem with Anthos 

The diagram below illustrates the high-level architecture of running Windows container-based workloads in an on-prem GKE cluster with Anthos. Windows server node-pools can be added to an existing or new Anthos cluster. Kubelet and Kube-proxy run natively on Windows nodes, allowing you to run mixed Windows and Linux containers in the same cluster. The admin cluster and the user cluster control plane continue to be Linux-based, providing you a consistent orchestration experience and management ease across Windows and Linux workloads.

Get started today

When considering modernizing your on-prem Windows estate, we recommend running Windows Server containers on Anthos in your own data center. If you are new to Anthos, the Anthos getting started page and the Coursera course on Architecting Hybrid Cloud with Anthos are good places to start. You can also find detailed documentation on our website, and our partners are eager to help you with any questions related to the published solutions, as is the GCP sales team. And as always, please don’t hesitate to reach out to us at anthos-onprem-windows@google.com if you have any feedback or need help unblocking your use case.

Google Cloud Results

• Reduced smart parking/smart city IoT installation and operational support effort by more than half
• Enabled development of a Smart Cloud IoT platform in just four months
• Democratised data access and use across the organisation
• Built core infrastructure in under 4 months

Smart Parking’s core product is a sensor-based system called SmartPark, used in environments such as shopping centres, airports, commercial parking operations, universities, and municipal streets.

Smart Parking has deployed over 50,000 sensors worldwide to support parking systems. It estimates at least 70% of production-scale and currently operating smart parking environments globally are using its technologies.

“Our products include ground sensors that use an advanced combination of infrared and magnetic technologies to reliably register a vehicle’s arrival and communicate to a gateway,” says Brian Granatir, Technical Team Lead, Smart Parking. This enables operators to gain live insights into parking environment usage and manage capacity. They can also provide automated guidance and signage to tell customers how many vacant spaces are available on each level of a parking structure or street area of a city.

Additional functionality enables parking operators to identify whether vehicles parked in limited-time spots have overstayed. The operators may notify inspectors who can undertake enforcement actions such as issuing infringement notices.

Another Smart Parking mobile product enables parking operators to photograph licence plates to identify vehicles that have overstayed. The business is also developing applications that enable users to view the number of parking spots available in nearby parking structures.

Smart Parking is extending its operations into smart city developments. The business views this as a natural progression as its parking systems establish a powerful common foundation from which to deliver many other services. “Parking is just part of a very complex ecosystem that requires sophisticated interoperability and generalised analytics,” John Heard, Chief Technology Officer, Smart Parking, explains.

“The biggest trend in the space is towards the Internet of Things (IoT)–the integration of a large number of generic and specialised devices,” Heard adds. “This has led us to shift from the idea of our devices being the centre of the universe. Instead, we put data intelligence at the core.”

In October 2016, Smart Parking decided to develop a SmartCloud platform that would enable cities to manage and react to information from a connected network of IoT devices. The business wanted to start by rolling out the platform based on an event-driven architecture that could connect any device that sends or receives a sequence of data events to existing Smart Parking customers.

The platform would enable Smart Parking to address a wide range of highly complex and interrelated interactions. “Internally, we use the example that if everyone leaves a parking structure at 3pm, what will be the impact on traffic?” Heard says. “So, how can we correlate multiple factors to answer a larger question?”

Heard’s team elected to deploy SmartCloud on Google Cloud Platform (GCP), due primarily to its support for sophisticated big data management and machine learning in a serverless architecture. Furthermore, Google technologies were proven to operate at scale and GCP had baked-in best practices that could manage internet-scale datasets in near real time.

“By running on Google, we were able to develop SmartCloud at an incredible pace,” Granatir says. “We built the core infrastructure in under four months.” Furthermore, GCP enables the SmartCloud platform to operate at metropolis scale, combining streams of distributed data to deliver an unprecedented view of the events and interactions taking place in a city.

Smart Parking now uses a wide range of GCP services to deliver SmartCloud. Google Cloud Dataflow provides processing and Google BigQuery provides management and analytics of big data. Google Cloud Pub/Sub provides real-time messaging and Google Cloud Functions enables a serverless environment to enable delivery of streaming data. Google Cloud IoT Core connects, manages, and ingests data from Smart Parking devices while Google Cloud Identity & Access Management enables Smart Parking to control access to information. Google Cloud Pub/Sub and Google Cloud Functions also support the event-driven architecture.

Installation time and operational burdens cut by at least half

Using Google Cloud IoT Core has cut Smart Parking’s parking and smart city system installation times by at least half and eliminated the need to build a registration system, a device authorisation system, and a feedback loop for each project. This delivers a streamlined and simpler methodology for site installations, which flows on to a dramatic decrease in the operational burdens involved in monitoring and maintaining deployments of all scales.

Using GCP has revolutionised Smart Parking’s software development processes. “We’re building and deploying systems at a speed I could never have imagined before Google Cloud Platform,” Granatir says. Furthermore, using GCP has made data accessible to anyone within the Smart Parking business, and this is being extended to customers via SmartCloud information services.

This democratised approach has already realised considerable benefits. “One of our project managers was experimenting with the Google Data Studio service for visualising data and was able to make a query about utilisation efficiency,” Heard adds. “We found that simply changing the maximum stay time by a small amount could hugely impact the percentage of parking utilisation and, therefore, throughput.

“This is an illustration of a significant insight by someone who simply had a hunch and quick access to mountains of live and accurate data.”

Transition to a data-intelligence company

Using GCP has enabled Smart Parking to chart a path from a sales and maintenance organisation to a smart cities data-intelligence company. “How? Simply by providing amazing services that focus on massive data volumes and sharing documentation that helps us leverage best practices,” Heard says. “Because of Google Cloud Platform and its pricing, we don’t walk in the shadow of giants, we ride on their shoulders.”

Smart Parking is now looking to further extend its use of Google services to add value to its smart parking and future smart city clients. “With Google Cloud Machine Learning Engine, we could gain the ability to identify trends across every customer parking site and make recommendations to operators,” Heard says. “For example, we could provide data-driven insight and advice to operators about how to adjust their parking spot time limits and guidance about how to transform throughput and convenience while increasing revenue.”

Heard concludes, “As a result of being able to dramatically decrease our traditional development burdens by using GCP, we now have a number of exciting new capabilities we have been able to bring in to our roadmap and expand the journey we are able to open up for our customers as well.”