This article is contributed. See the original author and article here.
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We continue to expand the Azure Marketplace ecosystem. For this volume, 97 new offers successfully met the onboarding criteria and went live. See details of the new offers below: |
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This article is contributed. See the original author and article here.
AI today is about scale: models with billions of parameters used by millions of people. Azure Machine Learning is built to support your delivery of AI-powered experiences at scale. With our notebook-based authoring experience, our low-code and no-code training platform, our responsible AI integrations, and our industry-leading ML Ops capabilities, we give you the ability to develop large machine learning models easily, responsibly, and reliably.
One key component of employing AI in your business is model serving. Once you have trained a model and assessed it per responsible machine learning principles, you need to quickly process requests for predictions, for many users at a time. While serving models on general-purpose CPUs can work well for less complex models serving fewer users, those of you with a significant reliance on real-time AI predictions have been asking us how you can leverage GPUs to scale more effectively.
That is why today, we are partnering with NVIDIA to announce the availability of the Triton Inference Server in Azure Machine Learning to deliver cost-effective, turnkey GPU inferencing.
There are three components to serving an AI model at scale: server, runtime, and hardware. This new Triton server, together with ONNX Runtime and NVIDIA GPUs on Azure, complements Azure Machine Learning’s support for developing AI models at scale by giving you the ability to serve AI models to many users cheaply and with low latency. Below, we go into detail about each of the three components to serving AI models at scale.
Triton Inference Server in Azure Machine Learning can, through server-side mini batching, achieve significantly higher throughput than can a general-purpose Python server like Flask.
Triton can support models in ONNX, PyTorch, TensorFlow, and Caffe2, giving your data scientists the freedom to explore any framework of interest to them during training time.
For even better performance, serve your models in ONNX Runtime, a high-performance runtime for both training (in preview) and inferencing.
ONNX Runtime is used by default when serving ONNX models in Triton, and you can convert PyTorch, TensorFlow, and Scikit-learn models to ONNX.
NVIDIA Tesla T4 GPUs in Azure provide a hardware-accelerated foundation for a wide variety of models and inferencing performance demands. The NC T4 v3 series is a new, lightweight GPU-accelerated VM, offering a cost-effective option for customers performing real-time or small batch inferencing who may not need the throughput afforded by larger GPU sizes such as the V100-powered ND v2 and NC v3-series VMs, and desire a wider regional deployment footprint.
The new NCasT4_v3 VMs are currently available for preview in the West US 2 region, with 1 to 4 NVIDIA Tesla T4 GPUs per VM, and will soon expand in availability with over a dozen planned regions across North America, Europe and Asia.
To learn more about NCasT4_v3-series virtual machines, visit the NCasT4_v3-series documentation.
Using Triton Inference Server with ONNX Runtime in Azure Machine Learning is simple. Assuming you have a Triton Model Repository with a parent directory triton and an Azure Machine Learning deploymentconfig.json, run the commands below to register your model and deploy a webservice.
![[dot] NET Modernization in a Day- 1-Day workshop.png](https://www.drware.com/wp-content/uploads/2020/10/large-201 "[dot] NET Modernization in a Day- 1-Day workshop.png")
az ml model register -n triton_model -p triton --model-framework=Multi
az ml model deploy -n triton-webservice -m triton_model:1 --dc deploymentconfig.json --compute-target aks-gpu
In this blog, you have seen how Azure Machine Learning can enable your business to serve large AI models to many users simultaneously. By bringing together a high-performance inference server, a high-performance runtime, and high-performance hardware, we give you the ability to serve many requests per second at millisecond latencies while saving money.
To try this new offering yourself:
You can also request access to the new NCasT4_v3 VM series (In Preview) by applying here.
This article is contributed. See the original author and article here.
During Microsoft Ignite 2020, we announced several new Microsoft Lists innovations coming later this year. In addition to this news, we produced six on-demand videos to provide a clear first look at these announcements plus showcase the broader context and value Lists brings to your productivity cloud investment.
Microsoft Lists is your smart information tracking app for Microsoft 365 and in Microsoft Teams. The below videos – including a few bonuses at the end – cover the breadth and depth of what Lists are and how it benefits individuals and teams track what matters most.
1 | “Get started with Microsoft Lists” by Lincoln DeMaris and Bharath Manoj Manda
In this session, Lincoln and Bharath zero in on how Lists evolve from SharePoint lists to empower individuals and teams to create, share and track information – including innovation in Microsoft Teams. You’ll see lots of demos highlighting numerous entry points and capabilities:
Learn more how to use Microsoft Lists.
2 | “Microsoft Teams ❤ Microsoft Lists” by Ansuman Acharya
Content collaboration meets information management meets conversation flexibility. Lists plus Teams brings it all together. Ansuman walks you through how you and your colleagues can best create, share and track lists and list items all from within the comfort of Microsoft Teams:
Learn how you can get started with Lists in Teams.
3 | “Organize business information with Microsoft Lists” by Miceile Barrett and Niket Jain
It’s time to make a list your own. Miceile and Niket show you how to use and create views, configure conditional formatting, adjust forms and more. You’ll come into this session new to Lists and leave a list maker:
Learn more how to create, change, or delete a view of a list.
4 | “Customize productivity apps with lists and libraries, Power Apps, and Power Automate” by Chakkaradeep (Chaks) Chinnakonda Chandran
No-code and low-code apps have been essential in SharePoint for a long time. Additional tools from the Power Platform – Power Apps and Power Automate – provide additional umph and capability. In this demo-heavy session, Chaks shows you how Microsoft Lists can connect with external data sources and help keep business on track:
Learn more how you can customize list forms with Power Apps and build custom workflows with Power Automate (approval example).
5 | “Designing the Microsoft Lists experience” by Ben Truelove and Christopher Wymer
Join in and learn how the Microsoft Design team behind Lists approached designing the Lists app. You’ll learn more about the problems, principles and approaches to the simple, beautiful user experience that helps people be productive every day, with a strong focus on accessibility and inclusion:
Learn more about Microsoft Design and the Fluent Design System.
6 | “Working with files and lists in Microsoft Teams” by Mark Kashman
Get the most of working with lists and files directly inside Microsoft Teams. I take you thru numerous scenarios when using the new Lists app in Teams and benefiting from the Files tab integration with the power of content services (powered by SharePoint) – access, share and collaborate on lists and files within your Teams channels:
Learn more about collaborating on files in Teams and using Lists in Teams.
Bonus related session | “Embrace a New Way of Work with Microsoft 365” Presented by Angela Byers, Shin-Yi Lim, and customers: Edward Kopp, Magnus Lidström.
In an unprecedented time of workplace transformation, opportunities to thrive belong to those who embrace and adapt to the new normal. How do we collaborate remotely, stay connected and produce excellent work as a team? How do we stay productive while working from home? Join Angela and Shin-Yi to learn a new way to think about and manage work with Microsoft 365. Learn how Microsoft 365 makes it easier for your team to organize, share and track lists, files and tasks all in one place – Microsoft Teams – so you save time and accomplish more together:
Watch it on the Microsoft Ignite website + the related Ask The Experts (ATE) sessions: ATE-DB136 & ATE-DB136-R1.
Bonus innovation snippet | “Project Nucleus” for Lists – “offline mode”
Take a “no audio” peek at Project Nucleus, as shown during Jeff Teper’s overview session, “Enabling collaboration, communication, and knowledge sharing with Microsoft Teams, SharePoint, Project Cortex, and more” [DB158].
In the future, you’ll be able to work with lists both online and offline with a faster user experience. The below video shows enhanced performance, first in online mode. The person then puts their device into “airplane mode” (offline) and can still perform actions like sorting, filtering, grouping, search and add new items to the list. [Note: related Microsoft 365 roadmap ID: 68809]
Project Nucleus will power the next generation of our web apps and portals. It will use intelligent sync to a database to keep a cache on the client that our web apps use to deliver a leapfrog in performance. All changes are synched with the local cache so Project Nucleus will enable you to work even when offline; changes are uploaded when you reconnect to Internet.
And like that, you and Your lists just got a whole lot smarter. :)
Thanks,
Mark Kashman, senior product manager – Microsoft Lists
This article is contributed. See the original author and article here.
This was a very interesting case like a puzzle. So I was working with my colleague Valentin Ursu from databricks team and we had this situation where the customer was able to connect to data bricks with JDBC but not with Synapse connector using like https://docs.databricks.com/data/data-sources/azure/synapse-analytics.html
The failure on ADW was the error 18456. State 8 – https://docs.microsoft.com/en-us/sql/relational-databases/errors-events/mssqlserver-18456-database-engine-error?view=sql-server-ver15
State 8 means password incorrect.
Databricks failed with an authentication error.
If we tried to connect to ADW with the same user and password using SSMS or ADF it worked. So how the password would be incorrect if I copy and paste in another platform it works.
We checked the firewall, syntax, connection string and it seems everything was correct.
So from both technologies ADW and databricks it seems there was not failure.
Note: If you click on SynapseSQL Pool Azure portal -> Connection strings you will have the syntax per drive.
So my colleague point to this and that was the problem.
The connection was been done by URL to ADW and the complexity of the password while encoding was leading to failure:
val sqlDwUrlSmall = "jdbc:sqlserver://" + dwServer + ":" + dwJdbcPort + ";database=" + dwDatabase
…
We asked them to try the follow:
.option("url", sqlDwUrlSmall)
.option("user", dwUser)
.option("password", dwPass)
…
So in other words a complex password with:.;:=?~^
it would become %3B%3A%3D%3F~%5E after encoding.
So basically when we are using URL the complexity of the password can lead to failures to connect.
Checkout about password complexity: https://docs.microsoft.com/en-us/sql/relational-databases/security/password-policy?view=azure-sqldw-latest
More information here:
https://docs.microsoft.com/en-us/sql/connect/jdbc/building-the-connection-url?view=sql-server-ver15
Thanks, Valentin!
That is it!
Liliam UK
Engineer
This article is contributed. See the original author and article here.
In today’s new world of work, finding ways to work better together no matter where team members are located is critical. That’s why we’re excited to announce our first-ever non-Microsoft integration for Visio with Atlassian Confluence, a remote-friendly team workspace that provides knowledge management, project collaboration, and a user-friendly social interface to help people share work and get feedback.
Using the Microsoft OneDrive for Business app for Confluence, you could already embed Word, Excel, or PowerPoint files stored in OneDrive or SharePoint to any Confluence page. Now, you can also easily add Visio diagrams to your Confluence pages. This app enables you to access and embed Visio files stored in OneDrive or SharePoint and open them for directly in a new browser tab. We’ve also ensured that your data remains secure—Confluence respects all privacy settings set in OneDrive and SharePoint, so no unauthorized users can access your content.
The ability to embed Visio files in your Confluence pages means that you can easily share and co-edit flowcharts and diagrams to collaborate on projects.
Once your IT administrator has enabled the Microsoft OneDrive for Business in Confluence macro, you can begin using it right away. From the Macros list in Confluence, simply search for OneDrive for Business.
Then, you can paste the OneDrive file URL for your Visio file into the area provided.
Confluence connects to your OneDrive account and displays a preview of the file. After you click Save, the Visio file appears on your Confluence page. You can select Open in new tab to begin editing.
This blog post is the second of four that dives into the update details from our Microsoft Ignite roundup blog. The remaining two will be published in the coming weeks, so keep checking our Tech Community Blogs site. In the meantime, please continue to send us your product feedback and ideas through UserVoice, or send a note to tellvisio@microsoft.com if you have specific questions or comments.
This article is contributed. See the original author and article here.
Microsoft Information Protection is a built-in, intelligent, unified, and extensible solution to protect sensitive data across your enterprise – in Microsoft 365 cloud services, on-premises, third-party SaaS applications, and more. Microsoft Information Protection provides a unified set of capabilities to know your data, protect your data, and prevent data loss across cloud services, devices, and on-premises file shares.
For data you have in file shares and SharePoint sites on-premises, scanner helps you discover the sensitive content and enables you to automatically label and protect it per policy you have configured in the Microsoft 365 compliance center. Every month, billions of files are scanned and protected on-premises using scanners by hundreds of different organizations to comply with internal policy and external regulations.
But customers looking to discover and protect sensitive data on premises are faced with two challenges. One is knowing where to start. This challenge is compounded by the fact that new content and new file shares are being constantly created by your end users. The second challenge is determining how to prioritize scanning among the thousands of repositories with petabytes of data. The new network discovery feature within a scanner, that is part of the latest Q3 preview, helps you tackle both these challenges. You can now use this as a step zero in your on-premises data discovery journey to map your file shares and identify the overexposed file shares to prioritize. The network discovery feature of the scanner enables you to target your IP ranges, or specific IPs, to find the shares hosted in these networks and use access information to identify overexposed file shares.
With this nee network discovery feature of the scanner it’s a matter of few hours to scan class B subnets with 65K IP addresses. Once the network discovery is finished the admins get a report of all shares, their share permissions, NTFS permissions and effective access to a standard user with access to the network. The report includes a hint on what shares are open to Everyone / Authenticated Users / Domain Users, which helps admins to identify assets that can be accessed by to anybody with physical access to your network. The next step will be triaging the discovered file repositories and adding them to a content scan job for deeper investigation.
The new methodology for on-premises scanner deployment includes four steps:
Figure 1: Microsoft Information Protection methodology for implementation of on-premises scanner for data at rest
So, how can you start? Download the latest preview release and upgrade your existing scanner deployment or install a fresh new deployment. Once the setup is completed you use PowerShell cmdlet to create the Network Discovery service and create a new Network scan job that contains a list of the IP ranges you would like to scan.
After configuring the network scan job and allocating a scanner cluster to execute it, the scan will start according to the schedule. Once the scan is completed all the discovered file shares will be listed under Repositories blade. You can filter the discovered repositories by setting “Discovered By” filter to “Network scan”.
Figure 2: Network scan results
You can now review the results and select the repositories you would like to add to the deep scan using the Content scan job.
Similar to the existing scanner deployments you can also use Network discovery with offline configuration or in air-gap scenarios. In this case your results will be available in the Report folder under scanner account profile at %localappdata%MicrosoftMSIPScannerReports.
As always, we encourage you to download the bits, deploy and share your feedback on Yammer community to help us to build the best product to meet your security needs.
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