AZ-500: Microsoft Azure Security Technologies Study Guide
The AZ-500 certification provides professionals with the skills and knowledge needed to secure Azure infrastructure, services, and data. The exam covers identity and access management, data protection, platform security, and governance in Azure. Learners can prepare for the exam with Microsoft's self-paced curriculum, instructor-led course, and documentation. The certification measures the learner’s knowledge of managing, monitoring, and implementing security for resources in Azure, multi-cloud, and hybrid environments. Azure Firewall, Key Vault, and Azure Active Directory are some of the topics covered in the exam.
Microsoft Azure Hub-Spoke model by Enterprise Design 1 of 4
Microsoft Virtual Datacenter Transition to Azure (Hybrid Cloud) Microsoft Azure Hub-Spoke Architecture This Enterprise reference architecture shows how to implement a hub-spoke topology in Azure. The hub is a virtual network (VNet) in Azure that acts as a central point of connectivity to your on-premises network. The spokes are VNets that peer with the hub, and can be used to isolate workloads. Traffic flows between the on-premises datacenter and the hub through an ExpressRoute or VPN gateway connection. Read the complete Blogpost Journey to the Azure Cloud here[Solved] Allow PIN support for Windows 10 devices
I want to allow my Windows 10 1909 (Hyper-V VM) to be able to use PIN for sign ins. I have created a non-administrator account and joined my VM during Windows installation to the AAD from the start. I also configured this for PIN policy in Windows 10 in Azure portal - Intune I created a group in Intune and put my VM device + User into that. then I assigned this profile that I created for PIN to that group. added my administrator user as the group owner. I've also read this article: https://support.microsoft.com/en-us/help/3201940/can-t-configure-a-pin-when-convenience-pin-and-hello-for-business-poli still, in my Windows 10 account settings, there is no sign of PIN. i've waited 2 hours, synced my device from AAD portal and also from Windows settings to receive the latest policies. still nothing. I'm running out of clues that why this is not working. any ideas? Thanks in advanceAnnouncing the Open-Source Release of ML Video Codec (MLVC)
Video codecs compress video for transmission or storage, reducing bandwidth and storage requirements. MLVC is a modern machine-learning-based codec that uses substantially less bandwidth than conventional codecs, improving streaming and video-call quality—especially on constrained or unreliable networks—while lowering delivery and storage costs. MLVC is the product iteration of DCVC (Deep Contextual Video Compression) family of NVC (Neural Video Codec), open sourced by Microsoft Research since 2021, with improved compression efficiency, real-time performance on commodity Neural Processing Units (NPUs), and cross-platform support. We recently published this work in the paper MLVC: Multi-platform Learned Video Codec for Real-World Deployment. We are releasing the source code because we believe the next generation of video coding will be built openly, and we want the broader community — researchers, video codec engineers, platform vendors, product teams, as well as general developer community — to build it with us. Why MLVC Traditional video codecs (e.g., H.264/AVC, H.265/HEVC) have served the industry for a long time, but each generation requires enormous engineering effort for incremental gains and needs dedicated hardware which takes years to become commonly available. MLVC replaces conventional primitives — motion estimation, transforms, entropy modeling — with end-to-end learned neural compression, trained directly against rate-distortion objectives, and run on general-purpose NPU devices. The table below compares MLVC to popular video codecs, showing its lower bitrate and resulting savings in bandwidth and storage. Resolution vs H.264 vs H.265 360p 87.8% 75.5% 540p 82.7% 65.4% For example, for 360p video at 30 fps, where H.264 requires 1 Mbps, MLVC requires roughly 122 kbps for equivalent quality — about one-eight the bitrate under real-time conditions. The inference compute was kept approximately equal for the 360p and 540p resolutions. These results are based on a P.910 subjective test and are based on the Video Conferencing Dataset (VCD) dataset that we developed and also recently released as an open-source project. The following video demo illustrates the extent of quality enhancement achieved by MLVC relative to H.265/HEVC at the same bitrate of 200kbps (please watch by opening in a new window for better demonstration) \ Beyond video compression efficiency, MLVC also offers: NPU-first design. MLVC is built to run almost entirely on the AI accelerators already shipping in modern devices — Apple Neural Engine, Qualcomm and Intel NPUs — at no more than 50% NPU utilization, leaving NPU headroom for the rest of the system. Real-time execution at the targeted operating points. Demonstrated 540p at 30 fps on Apple, Intel, and Qualcomm hardware. A scaling-law trajectory. Empirically, MLVC's coding efficiency improves with increased model capacity and additional training compute. Content-adaptive behavior out of the box, without hand-tuned Rate Distortion Optimization heuristics. Already running in Microsoft Teams MLVC is more than just a research concept for Microsoft. We are currently rolling it out in Microsoft Teams, where it is being validated on real peer-to-peer video calls with active telemetry and A/B testing. The integration runs alongside fallback to conventional video codecs for hardware or reliability constraints — the kind of mixed-environment deployment that real products need. The scaling and reliability insights from this rollout are shaping the codec and its roadmap. We welcome your contributions We can not cover every use case, every device class, or every content domain by ourselves. That is why we are open sourcing MLVC. If you work on: Streaming, Video On Demand (VOD), or live broadcast Real-time communication and conferencing Cloud gaming or remote rendering Surveillance, drones, or robotics Mobile capture, Augmented Reality (AR) / Virtual Reality (VR), or volumetric video Codec hardware, NPUs, or inference runtimes We would love your help. Contributions of all kinds are welcome, including model improvements, training recipes, new platform ports and conversion targets, runtime backends, domain-specific fine-tunes, evaluation tooling, bug reports, and feedback on what is missing for your scenario. We particularly welcome platform ports that expand NPU coverage and efficiency improvements that push the rate-distortion frontier. What's in the release The MLVC repository is available at https://github.com/microsoft/mlvc and shared under the MIT License. It includes: MLVC model source code of the full network architecture. Trained model weights ready to run. Training scripts used to produce shipping models. Training data collection documentation to help reproduce and improve MLVC. Platform conversion scripts to target different NPUs and runtimes. Issues and pull requests will be open from day one. A follow-up release will add a C++ codec library, simplifying integration into real-world applications. Where we're heading Our long-term goal with MLVC is to create an open, learned video codec that meets or exceeds the coding efficiency of the best conventional codecs across the full range of video content, runs efficiently on the AI hardware already shipping in client and cloud devices, scales with compute the way modern ML systems do, and evolves in the open at the pace of the ML community rather than the pace of standardization cycles. In the near term that means stabilizing 540p real-time performance, expanding hardware coverage, and improving loss resilience. In the medium term: higher resolution, e.g., 1080p, and broader streaming scenarios. In the long term: an open video codec ecosystem that meaningfully replaces legacy stacks where it makes sense to. We can't create the future of MLVC alone. We're glad you're here, and we are looking forward to building the next-generation video codec with you. Who are we MLVC is brought to you by the following awesome folks working on the project at Microsoft: Ross Cutler, Ando Saabas, Tanel Pärnamaa, Ardi Loot, Haiyan Xie, Lauri Ehrenpreis, Andrei Znobishchev, Martin Lumiste, Evgenii Indenbom, Yan Lu, Bin Li, Jiahao Li, Naba Kumar, Babak Naderi, Juhee Cho, Badal Yadav, Jinxin Zhou, Tianyu Ding, Patrick Gregory. — The MLVC Team, Microsoft
