Category Archives: Azure Cognitive Services

Accelerate Your AI with Machine Learning on Azure Data Box Edge

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.

Microsoft and BlackRock Announce Retirement Planning Partnership

At this point, the state of financial planning is a potential major crisis with current and future generations coming upon retirement age with little or no savings to account for.

As we’ve moved away from pensions of the old, the responsibility held previously by the companies, has now shifted to individuals having to invest and save on their own to ensure they’re set up after they retire.

I wanted to share a recent press release from Microsoft who announced that they created a partnership with BlackRock to help reimagine the way people manage their retirement planning. BlackRock is a world leader in wealth management, including providing solutions to consumers and currently manages approximately 6.5 trillion in assets for investors worldwide.

The goal of this alliance is to find ways for people to interact with their retirement assets more, so they know what kind of contributions they’re making. BlackRock will design and manage a suite of next generation investment tools that aim to provide a ‘lifetime’ of income in retirement. This would be made available to US workers through their employer’s workplace savings plan.

The press release did not share much detail about what exactly the two firms will partner on, but the following is a quote from Microsoft CEO, Satya Nadella: “Together with BlackRock, we will apply the power of the cloud and AI to introduce new solutions that address this important challenge and reimagine retirement planning.”

As we know, AI, deep learning and machine learning and all their related technologies, can have a profound impact on information gathering, processing and the intelligence we can extract from it. This helps us make better decisions.

The idea here is to offer technology options to businesses for their employees to consume and promote fiduciary responsibility. There will be more complex options that have been shunned previously by employers because of their complexity and costliness.

BlackRock has shown that they want to move their technology footprint forward with acquisitions and investments in firms in recent years. In 2015, they acquired a robo advisor company, as well as invested in Acorns, a company which helps millennials save their spare change to put it into a savings account.

Last year, BlackRock acquired Investment, a company that gives them more sophisticated online investment tooling. It is also believed that additional partnerships will come along to help support any of these new investment options, the plans and the employees.

When it comes to how the world is changing, AI is thought to be one of the biggest conversations occurring in 2019. At the heart of AI is data—data quality and consistency. These important factors are something we focus on at Pragmatic Works, as well as knowing that this is what our clients need to rely on.

This press release shows where we’re going with some of the AI technology that’s a huge topic of conversations in organizations today.

Overview and Benefits of Azure Cognitive Services

With Artificial Intelligence and Machine Learning, the possibilities for your applications are endless. Would you like to be able to infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication, all without having any data science expertise?

Continue reading Overview and Benefits of Azure Cognitive Services