by Contributed | Nov 11, 2021 | Technology
This article is contributed. See the original author and article here.
We are excited to be partnering with SWOOP Analytics to host our 2021 Yammer Community Festival on December 8th-9th! This event has been curated and designed for our customers, for you to learn from each another and hear how to build culture and community in your own organizations. Whether you are just launching your Yammer network and not sure where to begin or are curious to hear what’s coming down the product pipeline, there are sessions for you!
Agenda
This virtual event will span across European, Americas, and Asia Pacific time zones. Look at the agenda to see an idea of the types of stories and sessions that will be available. All sessions will be recorded to be able to watch on demand following the event.
*Exact timing and content subject to change.
Speakers
We have an incredible line up of Yammer customer speakers that have a variety of experiences and types of organizations from across the world. They’ll share best practices, lessons learned and provide practical guidance that you’ll be able to take and use at your organization. You’ll also hear directly from Microsoft , including the product teams who build Yammer and learn more about the future of products like Microsoft Viva. You might see new faces and familiar brands that have great stories to share. Take a closer look at all the presenters here.
Nominate your colleagues for the Community Champion Award
The Yammer Community Champion Award is an opportunity to recognize passionate community managers around the world who are committed to employee engagement, knowledge sharing, and collaboration in their Yammer networks and communities.
As part of our first Yammer Community Festival, we will announce regional winners of the Yammer Community Champion Award.
Can you think of anyone who deserves this title? Tell us who you think should win this award!
There will be one winner per regional session and winners will be contacted in advance of session that they have been ‘shortlisted’ as a top contender for the Yammer Community Champion Award. This is to ensure the winners are present to virtually accept their award. Based on network size, the number of nominations per organization is not limited as there may be many opportunities within a company to submit a nomination.
Register today!
We’re running the event as an interactive Microsoft Teams meeting with Q&A enabled. Bring your questions and ideas as we want to make the chat component active, with many voices heard. Speakers are there to prompt conversations in chat and be prepared to unmute!
“We are so excited to present the future of Yammer to this community. We’ve heard from customers like you, that you want to connect and learn from the Yammer community of customers that exist across the globe. This is a great opportunity to showcase your success and learn from others in the community. We look forward to seeing you all at the festival!”
MURALI SITARAM
CVP Yammer+M365 Groups, Microsoft
Save your seat and register now!
FAQ
How much does this cost?
This event is free so invite your whole team!
Can I attend a session outside of my time zone?
Yes! Feel free to attend any session that fits your schedule, regardless of your location.
Will sessions be recorded?
Yes, sessions will be recorded and made available after the event.
How many people can I nominate for the Community Champion Award?
We do not have a set limit for number of nominations by organization or person. Feel free to nominate all the Community Champions you know!
What do the Community Champion Award members win?
There will be custom swag sent to each award winner.
Who should attend?
All roles and departments are welcome. Customer speakers will be a variety of backgrounds and roles within an organization and you’ll be sure to find similar roles that support the work you do, regardless if you are in marketing, communications, sales, product development or IT.
by Scott Muniz | Nov 11, 2021 | Security, Technology
This article is contributed. See the original author and article here.
VMware has released a security advisory to address a privilege escalation vulnerability in vCenter Server and Cloud Foundation. An attacker could exploit this vulnerability to take control of an affected system.
CISA encourages users and administrators to review VMware Security Advisory VMSA-2021-0025 and apply the necessary workaround.
by Contributed | Nov 10, 2021 | Technology
This article is contributed. See the original author and article here.
We announce here that Microsoft’s Automated Machine Learning, with nearly default settings, achieves a score in the 99th percentile of private leaderboard entries for the high-profile M5 forecasting competition. Customers use Automated Machine Learning (AutoML) for ML applications in regression, classification, and time series forecasting. For example, The Kantar Group leverages AutoML for churn analysis, allowing clients to boost customer loyalty and increase their revenue.
Our M5 result demonstrates the power and effectiveness of our Many Models Solution which combines classical time-series algorithms and modern machine learning methods. Many Models is used in production pipelines by customers such as AGL, Adamed, and Oriflame for demand forecasting applications. We also use our open-source Responsible AI tools to understand how the model leverages information in the training data. All computations take place on our scalable, cloud-based Azure Machine Learning platform.
The M5 Competition
The M5 Competition, the fifth iteration of the Makridakis time-series forecasting competition, provides a useful benchmark for retail forecasting methods. The data contains historical daily sales information for about 3,000 products from 10 different Wal-Mart retail store locations. As is often the case in retail scenarios, the data has hierarchical structure along product catalog and geographic dimensions. Data features like sales price, SNAP (food stamp) eligibility, and calendar events are provided by the organizers in addition to historical sales. The accuracy track of the competition evaluates 28-day-ahead forecasts for 30,490 store-product combinations. With submissions from over 5,000 teams and 24 baseline models, the competition provides a rich set of comparisons between different modeling strategies.
Modeling Strategy
There are myriad approaches to modeling the M5 data, especially given its hierarchical structure. Since our goal is to demonstrate an automated solution, we executed what we considered the most simple strategy: build a model for each individual store-product combination. The result is a composite model with 30,490 constituent time-series models. Our Many Models Solution, born out of deep engagement with customers, is precisely suited to this task.
Many Models Flow Map
The Many Models accelerator runs independent Automated Machine Learning (AutoML) jobs on each store-product time-series, creating a model dictionary over the entire dataset. In turn, each AutoML job generates engineered features and sweeps over model classes and hyperparameters using a novel collaborative filtering algorithm. AutoML then selects the best model for each time-series via temporal cross-validation. Training and scoring are data-parallel operations for Many Models and easily scalable on Azure-managed compute resources.
Understanding the Final Model
The final composite model is a mix of three model types: classical time-series models, machine learning (ML) regression models, and ensembles which can contain multiple models from either or both of the first two types. AutoML creates the ensembles from weighted combinations of top performing time-series and ML models found during sweeping. Naturally, the ensemble models are often the best models for a given store-product combo.
The chart above shows that two-thirds of the selected models are ensembles, with classical time-series and ML models making up approximately equal portions of the remainder.
We get a more detailed view of the composite model by breaking into model sub-types. AutoML sweeps over three ML regression subtypes: regularized linear models, tree-based models, and Facebook’s Prophet model. Classical algorithms include Holt-Winters Exponential Smoothing, ARIMAX (ARIMA with regressors), and a suite of “Naive”, or persistence, models. Ensembles are weighted combinations of these sub-types.
The proportions of subtypes in the full composite model are shown above, where ensemble weights are used to apportion subtypes from each ensemble. Tree-based models like Random Forest and XGBoost that are capable of learning complex, non-linear patterns are a plurality. However, relatively simple linear and Naive time-series models are also quite common!
Feature Importance
Most of AutoML’s models can make use of the data features beyond the historical sales, so we find yet more insight into the composite model by examining the impact, or importance, of these features relative to the model’s predictions. A common way to quantify feature importance is with game-theoretic Shapley value estimates. AutoML optionally calculates these for the best model selected from sweeping, so we make use of them here by aggregating values over all models in the composite.
In the feature importance chart, we distinguish between features present in the original dataset, such as price, and those engineered by AutoML to aid model accuracy. Evidently, engineered features associated with the calendar and a seasonal decomposition make the most impact on predictions. The seasonal decomposition is derived from weekly sales patterns detected by AutoML. Price is the most important of the original features which is expected in retail scenarios given the likely significant effects of price on demand.
The Value of AutoML and Many Models
Our automatically tuned composite model performs exceedingly well on the M5 data – better than 99% of the other competition entries. Many of these teams spent weeks tuning their models. Despite this excellent result, it is important to note that no single modeling approach will always be the best. In this case, we achieved great accuracy with an assumption that the product-store time-series could be modeled independently of one another. This implies that the dynamics driving changes across sales at different stores and products may vary widely. We’ve learned from several successful engagements with our enterprise customers that the Many Models approach achieves good accuracy and scales well across other forecasting scenarios as well.
Special thanks to Sabina Cartacio for contributing text and editorial guidance.
by Scott Muniz | Nov 9, 2021 | Security, Technology
This article is contributed. See the original author and article here.
Microsoft has released updates to address multiple vulnerabilities in Microsoft software. An attacker can exploit some of these vulnerabilities to take control of an affected system.
CISA encourages users and administrators to review Microsoft’s November 2021 Security Update Summary and Deployment Information and apply the necessary updates.
by Contributed | Nov 9, 2021 | Technology
This article is contributed. See the original author and article here.
https://www.youtube-nocookie.com/embed/hsNc_QjYwfw
At the beginning of November, Microsoft had their second Ignite of the year, announcing or further clarifying details around many of the latest and near-immediate future features expected to rollout to Microsoft 365. However, as many of the US Federal cloud tenants see features months (if not longer) after they hit the commercial tenant, these users are often left wondering “what’s next for us” instead of having the same excitement commercial tenant owners have coming out of these conferences.
In this episode, we meet with Microsoft architect John Moh (LinkedIn) to discuss our favorite ways to stay up to date on what’s available to us in the GCC, GCC-H, and DOD tenants!
Government Community Events
In the News
Roadmap Update
Today’s Discussion
Today’s Panelists
Today’s panelists can be found on Twitter if you want to connect with them further!
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