Microsoft in the News
Today’s installment is all about a not-so-new buzzword(s): Intelligent Edge.
The Intelligent Edge refers to a place, like the cutting edge. The Intelligent Edge is where the action is. This can refer to the smart watch on your wrist, a sensor placed in a vineyard, a monitor under the hood of your car, in your glasses or clothes, a highway, or anywhere an IoT device is.
The intelligent part refers to the devices use of AI to sense what is happening at the edge and act on that data. Because these devices are connected to each other and to the cloud, and are able to process the data in real time, they can take immediate action.
An excellent example of this would be the pre-crash safety system built into modern vehicles. Although your car is connected to the cloud, the pre-crash safety system is able to make decisions without having to wait for Azure to process the data. When an imminent crash is detected, seat belts are pre-tensioned, brakes may be applied, airbags may be deployed optimally (depending on how many people are in the car and where they are sitting), seats may be adjusted, and so much more.
The Intelligent Edge has become so pervasive that it is starting to blend with the environment. Is it a lightbulb, or a computer? Is it just and espresso machine?
Microsoft has invested over $5 billion to ensure it is there at the edge.
TDSP Lifecycle – Wrap-up
It is important to note that each of the arrows in the diagram point in both directions. That is because each stage is iterative. If you think you are going to proceed through this process and get it right the first time, you haven’t done Data Science. There is a lot of trial and error involved, and a lot of learning what works along the way. Persistence is a required trait for any data scientist.
As fun as this has been, our walk through the TDSP forest has come to an end. This would be where some walk-through examples would be really useful in order to drive us home.
Fortunately, Microsoft agrees and has created some on-line walkthroughs showing how to actually put into practice all the things you have learned about TDSP. You can find them here: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/walkthroughs
If you are interested in doing some on-line training, you can choose training for data scientists or for DevOps. Microsoft provides a self-study guide where you can follow paths on a variety of topics and even check your understanding using available projects. All this can be found here: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/team-data-science-process-for-data-scientists
