MVRDV: the cloud

This blog post, authored by Luca Mezzalira, Federica Ciuffo, Laura Hyatt, Vittorio Denti, and Zamira Jaupaj, focuses on the benefits and resources for using containers on Amazon Web Services (AWS) for cloud-native application development and modernization. It highlights that most AWS customers building or modernizing applications choose containers for microservices to accelerate innovation, reduce time to market, and lower total cost of ownership. The post emphasizes that using containers with AWS services provides enhanced portability, scalability, flexibility, load balancing, auto-scaling, and service discovery, simplifying application deployment and management at scale. The article introduces several key resources to help users get started with containers on AWS. The first resource is the Container Build Lens, a whitepaper that is part of the AWS Well-Architected Framework. This document offers general design principles, best practices, and implementation guidance for containers, structured around the Six Pillars of the Well-Architected Framework, to help customers review and improve their cloud architectures and understand the business impact of their design choices. Another significant resource presented is the EKS Workshop, designed to familiarize users with Amazon Elastic Kubernetes Service (Amazon EKS). This workshop provides practical, real-world use cases and is organized into high-level learning modules such as Networking, Security, and DevOps Automation. These modules are further divided into standalone labs focusing on specific features, tools, or use cases. For building environments, the post recommends Amazon EKS Blueprints, a collection of Infrastructure as Code (IaC) modules available in Terraform and CDK, which facilitate configuring and deploying consistent, best-practice Amazon EKS clusters across various accounts and regions. Additionally, the blog post highlights an article titled "Architecting for resiliency on AWS App Runner." This resource demonstrates how to design highly available and resilient applications using AWS App Runner. It explains that App Runner simplifies the complexities of running containerized applications by abstracting away the underlying cloud resources, managing aspects like load balancers, TLS certificates, auto-scaling, logs, and metrics. This allows developers to concentrate on implementing business logic within a scalable and elastic environment. Finally, the article addresses the crucial aspect of security with a video session titled "Securing Kubernetes: How to address Kubernetes attack vectors." This session covers the fundamentals of Kubernetes architecture and common attack vectors. It also details the security controls offered by Amazon EKS and provides strategies to mitigate risks for Kubernetes-based workloads. The authors, all AWS experts, contributed their knowledge in areas ranging from solution architecture and container services to machine learning and enterprise solutions, ensuring a comprehensive overview of container technologies on AWS. #AWS #Containers #Kubernetes #AmazonEKS #AWSWellArchitected #CloudNative #Microservices #DevOps #CloudSecurity

Microsoft Digital has developed an innovative solution, Space Busyness Insights, to optimize space utilization and enhance employee experience in its new headquarters buildings in Redmond, Washington. The solution leverages existing Wi-Fi Wireless Access Point (WAP) infrastructure to collect data on people density and space usage, thereby eliminating the need for costly new IoT sensor installations. This approach aims to address key questions for modern workplaces: how to effectively use building spaces, reduce IoT sensor costs, and protect employee privacy while complying with regulations. The Space Busyness Insights solution integrates artificial intelligence (AI) and machine learning (ML) capabilities to analyze raw WAP data. This analysis provides real-time insights into identifying unused areas, occupied spaces, crowd density, and the availability of common areas like kitchenettes and conference rooms. Employees can access this real-time information to make informed decisions about using shared spaces, while facility managers can optimize space allocation, repurpose areas, and plan for future real estate needs. A crucial aspect of this solution is its cost-effectiveness; by reusing existing WAP devices, Microsoft saved approximately $3 million in hardware costs for the East Campus buildings alone. To implement this system, Microsoft Digital collaborated with its Infrastructure and Security team, internal facilities managers, and InnerSpace, a third-party vendor. InnerSpace provides the AI and ML logic to process the raw WAP data, triangulating device locations to estimate people density. InnerSpace utilizes Microsoft Azure services, including Azure Kubernetes for elastic capacity, Azure App Services for web-based tooling, and Azure Container Instances for deploying containerized subsystems. The system also incorporates proprietary AI logic to prevent double-counting individuals who may carry multiple Wi-Fi-enabled devices. Protecting employee privacy and ensuring compliance with privacy regulations are paramount to the solution's design. The system hashes Media Access Control (MAC) addresses of devices to anonymize data before it is sent to InnerSpace. Microsoft conducts stringent architectural and data privacy reviews to prevent the leakage of identifiable personal information. Only necessary, anonymized data is shared with InnerSpace via a federated authentication mechanism through Microsoft's Device Management Services (DMS) Azure Event Hub. InnerSpace then processes this data, correlating it with building maps and AP locations, and returns people-count information to Microsoft. The benefits of this solution extend beyond cost savings and operational efficiency. It enables data-driven decisions for space planning, replacing subjective guesswork with clear-cut data. The system allows for holistic analysis by potentially combining WAP data with other sensor signals, such as lighting and air quality. Its rapid deployment process, requiring only configuration updates for new buildings, facilitates scalability. Microsoft is already using this solution in seven buildings and plans to integrate it into its upcoming product, Microsoft Places, to provide a holistic view of physical and digital spaces and their usage within organizations. The company believes other organizations can benefit from this approach, which prioritizes existing infrastructure, reduces dependency on external sensors, protects privacy, and offers intuitive data access. #SpaceManagement #WiFiData #MicrosoftDigital #Azure #ArtificialIntelligence #PrivacyProtection #CostOptimization #SmartBuildings #DigitalTransformation

The traditional process of creating high-quality 3D content is known to be complex, time-consuming, and resource-intensive, demanding specialized skills for 3D modeling, texturing, and scene assembly. This is particularly challenging when digitally recreating real-world elements. Reality capture technologies, such as LiDAR scanning, Photogrammetry, and Neural Radiance Fields (NeRFs), offer a simplified solution by digitizing real-world objects or environments through the capture of multiple perspectives to estimate scene depth and geometry. While these automated approaches bypass manual 3D modeling and produce high-quality results faster, they typically involve expensive, specialized hardware, are compute-intensive, and often yield poor real-time rendering performance. Recently, 3D Gaussian Splatting emerged as a new technique with the potential to accelerate 3D scene reconstruction and rasterization while maintaining high quality. Before delving into this technique, it's crucial to understand 3D reconstruction, which involves recreating a 3D scene or object from 2D inputs like photos or video. Unlike LiDAR, which provides explicit depth data, 3D reconstruction infers depth from 2D images, employing computer vision techniques like multi-view stereo and structure from motion. Photogrammetry, an older method, uses these techniques to generate a point cloud, which is then converted into a polygon-based 3D mesh. NeRFs, introduced in 2020, represent scenes as volumes using neural networks to estimate light radiance, offering faster generation but sometimes inferior detail compared to photogrammetry. 3D Gaussian Splatting, introduced in 2023, builds on NeRFs but generates novel viewpoints by populating 3D space with view-dependent “gaussians.” These fuzzy 3D primitives, with adjustable colors, densities, and positions, mimic light behavior to create volumetric representations. Unlike NeRFs, 3D Gaussian Splatting avoids neural networks, instead utilizing traditional machine learning optimization methods like stochastic gradient descent, making it more computationally efficient. This technique provides photorealistic 3D reconstruction with improved visual quality and faster generation times, reducing noise and handling challenging elements like transparency and reflectivity more robustly than previous methods. The benefits of 3D Gaussian Splatting include shorter generation times due to direct rasterization of gaussians, superior real-time performance enabled by its volumetric, point cloud-based nature, and more robust outputs with fewer artifacts. These advantages significantly lower the barrier to entry for 3D asset creation, allowing non-specialists to create complex 3D objects using only a smartphone camera and a 3D reconstruction pipeline. It also increases accessibility to interactive, real-time 3D content on consumer devices, mobile platforms, and AR/VR, even without expensive accelerated computing. This technology could transform industries like virtual production, digital twins, and e-commerce product visualization by providing scalable, distributable, and performant interactive 3D experiences. Deploying 3D Gaussian Splatting at scale can leverage cloud infrastructure such as AWS for asset generation, management, and global distribution. An example workflow on AWS involves ingesting 2D media, processing it through modular pipelines (image processing, structure from motion, 3D Gaussian Splatting training, and viewing), and outputting a 3D object viewable in a web browser. This workflow can be implemented using services like AWS Cloud Development Kit (CDK) for infrastructure deployment, AWS Systems Manager Parameter Store for artifact storage, Amazon S3 and CloudFront for hosting and distribution, Amazon Cognito for authentication, Amazon DynamoDB for data storage, AWS Step Functions for workflow orchestration, Amazon ECS and ECR for containerized processing, and Amazon SNS for notifications. AWS services also support elastic processing, content storage, graphics-accelerated computing (e.g., Amazon EC2 G6 instances), content delivery, analytics (e.g., Kinesis, Athena, QuickSight), AI/ML integration (e.g., SageMaker, Bedrock), and digital twin applications (e.g., AWS IoT TwinMaker). The field continues to evolve with contributions from NVIDIA (Text-to-4D with Dynamic 3D Gaussians) and Meta (Robust Gaussian Splatting), highlighting ongoing advancements in integrating AI diffusion models and improving robustness for real-world captures. #3DGaussianSplatting #3DReconstruction #SpatialComputing #AWSCloud #RealTimeRendering #ContentCreation #MachineLearning #ARVR #DigitalTwin