Azure Flash Friday
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Contacts
Rick Weyenberg email: [email protected] twitter: @codeboarder
Mark Garner email: [email protected] twitter: @mgarner
website: www.azureflashfriday.com twitter: @azureflashfri
Up-Coming Events
Minnesota Azure Users Group
July 6th, 2017
@Microsoft office 3601 W 76th, Edina, MN
For more information:
https://aka.ms/mplsazug
ASOS: How they migrated from local monolith to microservices in Azure
https://azure.microsoft.com/en-us/blog/asos-how-they-migrated-from-local-monolith-to-microservicesin-azure/
Today we are kicking of a new series on Microsoft Mechanics called "How we built it" to share the realworld architectural back stories and best practices as told by the technology architects in our customer
organizations.
We kick off the series with lead architect, Dave Green, from British online fashion retailer ASOS, to take
a closer look at their design goals and approach for moving from a locally-operated monolith to a fully
architected built-for-Cloud online retail system.
Deep learning for predictive maintenance with Long Short Term Memory
Networks
https://azure.microsoft.com/en-us/blog/deep-learning-for-predictive-maintenance/
Deep learning has proven to show superior performance in certain areas such as object recognition and
image classification. It has also gained popularity in other domains such as finance where time-series
data plays an important role. Similarly, in predictive maintenance, the data is collected over time to
monitor the state of an asset with the goal of finding patterns to predict failures which can benefit from
certain deep learning algorithms. Among the deep learning networks, Long Short Term Memory (LSTM)
networks are especially appealing to the predictive maintenance domain since they are very good at
learning from sequences. This fact lends itself to their applications using time series data by making it
possible to look back for longer periods of time to detect failure patterns.
GDPR Questions? Azure has answers
https://azure.microsoft.com/en-us/blog/gdpr-questions-azure-has-answers/
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Please have a look at our white paper How Microsoft Azure Can Help Organizations Become Compliant
with the EU General Data Protection Regulation to gain an understanding of how your organization can
use currently available features in Azure to optimize your preparation for GDPR compliance. We are
here to help you with your compliance efforts in the face of the coming EU law.
Azure Site Recovery now supports managed disks
https://azure.microsoft.com/en-us/blog/managed-disks-with-azure-site-recovery/
Azure Site Recovery (ASR) now supports managed disks. This follows the announcement of Azure’s
support for managed disks in February. With the integration of managed disks into ASR, you can attach
managed disks to your machines during a failover or migration to Azure.
Managed disks provide the following advantages:
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Simplified disk management for Azure IaaS VMs by removing the hassle of managing storage
accounts for your machines after failover to Azure.
Improved reliability for Availability Sets by ensuring that the disks of the failed over VMs are
automatically placed in different storage scale units (stamps) to avoid single points of failure.
Mesosphere DCOS, Azure, Docker, VMware and everything between –
Architecture and CI/CD Flow
https://azure.microsoft.com/en-us/blog/mesosphere-dcos-azure-docker-vmware-and-everythingbetween-architecture-and-ci-cd-flow/
These days, I try to be involved in any Containers, DevOps, Automation, etc. related discussion. Part of
my role is to consult my customers around how to architect their containers platform and orchestration
tools in Azure. But what happens when you have a chance to do something cool like architecting a
solution which involves Mesosphere DC/OS, Azure Container Service, Azure Container Registry, Docker,
and VMware vSphere?! Let’s find out…
In this first multiple-part blog post series I will describe the motivation behind it, the requirements and
constraints, architecture, and of course the “how to” – let’s begin.
Predictive maintenance using PySpark
https://azure.microsoft.com/en-us/blog/predictive-maintenance-using-pyspark/
Predictive maintenance is one of the most common machine learning use cases and with the latest
advancements in information technology, the volume of stored data is growing faster in this domain
than ever before which makes it necessary to leverage big data analytic capabilities to efficiently
transform large amounts of data into business intelligence. Microsoft has published a series of learning
materials including blogs, solution templates, modeling guides and sample tutorials in the domain of
predictive maintenance. Recently, we extended those materials by providing a detailed step-by-step
tutorial of using Spark Python API PySpark to demonstrate how to approach predictive maintenance for
big data scenarios. The tutorial covers typical data science steps such as data ingestion, cleansing,
feature engineering and model development.
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Cloud Design Patterns
https://docs.microsoft.com/en-us/azure/architecture/patterns/
Great reference link for design patterns with code samples.
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Looking for Stickers?
http://www.redbubble.com/people/codeboarder?asc=u
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