AI solutions are exploding, and Azure has the most complete offering of any cloud provider! Watch this video to get started with our API based Cognitive Services in Azure and a sample architecture of how to employ them with the Azure Bot Service. Azure Cognitive Services are cloud-based services with REST APIs and client library SDKs available to help you build cognitive intelligence into your applications.
You can add cognitive features to your applications without having artificial intelligence (AI) or data science skills. Azure Cognitive Services comprise various AI services that enable you to build cognitive solutions that can see, hear, speak, understand, and even make decisions. Azure Bot Service enables you to build intelligent, enterprise-grade bots with ownership and control of your data. Begin with a simple Q&A bot or build a sophisticated virtual assistant.
you’re like most, security is at the forefront of your mind for your
organization. You need the right tools and the right team to keep up
with the balance of increasing number of sophisticated threats and with
security teams being inundated with requests and alerts.
Today I’d like to tell you about Microsoft’s reimagined SIEM tool Azure Sentinel.
Over the past 10 to 15 years, Security Information and Event Management
(SIEM) has become extremely popular as an aggregation solution for
security and events that happen in our network.
There are also software tools, hardware appliances and managed
service providers that can help support your corporate needs to better
understand the level of risks in real-time and over a span of time. They
do things such as log aggregation, event correlation and forensic
analysis and offer features for alerting, dashboarding and compliance
These are great resources to help secure our environment, our users and devices. But unfortunately, the reality is security
teams are being inundated with requests and alerts. Compile this with
the noteworthy shortage of security professionals in the world – an
estimated 3.5 million unfilled security jobs by 2021 – this is a major
Microsoft decided to take a different approach with Azure Sentinel. Azure Sentinel provides intelligent security analytics at cloud scale for your entire enterprise. It makes it easy to collect data across your entire hybrid organization on any cloud, from devices to users to applications to servers. Azure Sentinel uses the power of AI to ensure you’re quickly identifying real threats.
With this tool:
You’ll eliminate the burden of traditional SIEMs as you’re eliminating the need to spend time on setting up, maintaining and having to scale the infrastructure to support other SIEMs.
Since it’s built on Azure, it offers virtually limitless cloud scale while addressing all your security needs.
Now let’s talk cost. Traditional SIEMs have proven to be expensive to
own and operate, often requiring you to commit upfront and incur high
cost for infrastructure maintenance and data ingestion. With Sentinel, you pay for what you use with no up-front costs. Even better, because
of Microsoft’s relationships with so many enterprise vendors (and more
partners being added) it easily connects to popular solutions, including Palo Alto networks, F5 networks, Symantec and Checkpoint offerings.
Azure Sentinel integrates with Microsoft Graph Security API,
enabling you to import your own threat intelligence feeds and to
customize threat detection and alert rules. There are custom dashboards that give you a view to allow you to optimize whatever your specific use case is.
Lastly, if you’d like to try this out for free, Microsoft is
allowing you to connect to your Office 365 tenant to do some testing and
check it out in greater detail. This product is currently in
preview, so there may be some kinks but I’m looking forward to seeing
how it develops in the future, as a true enterprise-class security
solution for your environment, whether in the cloud, on premises, in
data centers or remote users or devices.
In some past blogs I’ve discussed Azure Data Box and how the Data Box family has expanded. Today I’ll talk about Azure Data Box Edge (in preview) and elaborate on the machine learning service that it provides in your premises with the power of Azure behind it.
If you don’t know, Azure Data Box Edge is a physical hardware device that sits in your environment and collects data from environment sources like IOT data and other sources where you might take advantage of the AI features offered by the device. It then takes the data and sends it to Azure for more processing, storage or reporting purposes.
Microsoft recently announced Azure Machine Learning hardware accelerated models provided by Project Brain Wave on the Data Box Edge. Because most of our data is in real world applications and used at the edge of our networks – like image and videos collected from factories, retail stores or hospitals – it can now be used for things such as manufacturing defect analysis or inventory out of stock detection in diagnostics.
By applying machine learning models to the data on Data Box Edge, it provides lower latency (and savings on bandwidth cost) as we don’t have to send all the data to Azure for analysis. But it still offers that real time insight and speed to action for critical business decisions.
You can enable data scientists to simplify and accelerate the building, training and deployment of machine learning models using the Azure Machine Learning Service which is already generally available. They can access all these capabilities in their favorite Python environment, using the latest open source frameworks such as PyTorch, TensorFlow and sci-kit-learn.
These models can run on CPUs and GPUs, but this preview expands that out to field programmable gate array processes (FPGA), which is the processor on the Data Box Edge.
The preview is currently a bit limited but, in this case, you’re able to enhance the Azure Machine Learning Service by training a TensorFlow model for image classification scenarios. So, you would containerize that model in a docker container and then deploy it to the Data Box Edge IOT hub.
A good use case for this is if you’re using AI models for quality control purposes. Let’s say you know what a finished product should look like and what the quality specs are, and you build a model defining those parameters. Then you take an image of that product as it comes off the assembly line; now you can send those images to the Data Box Edge in your environment and more quickly capture defects.
Now you’re finding the root cause of defects quicker and throwing away fewer defective products and therefore, saving money. I’m looking forward to seeing how enterprises are going to leverage this awesome technology.