Welcome back to our series of blog posts for #AI and the services provided by Microsoft to support artificial intelligence in Azure and Power Platform. In this article, we will delve into the foundation of every artificial intelligence software, Machine learning.
Question: “How do machines learn?”
Answer: “From data.”
In today's world we are producing a huge volume of data from our phones, photos, messages, business applications that we use daily. Machine learning models are using that data as their food to train and recognize patterns. It is a dynamic approach that empowers the machine to make predictions and improve their performance based on the data they encounter. By leveraging algorithms and statistical models, machines can autonomously learn from examples and refine their behavior over time.
For example, we have fleets of different vehicles. We categorize and label each vehicle type. Then we feed our labeled dataset into a model and initiate the training process. During training, the model learns to recognize distinguishing features and patterns associated with each vehicle type. So, next time when a new vehicle arrives in our fleet, our model can automatically identify its type, streamlining the process of managing and organizing our vehicle inventory.
Microsoft Azure provides the Azure Machine Learning service - a cloud-based platform for creating, managing, and publishing machine learning models. Azure Machine Learning provides the following features and capabilities:
|
Feature |
Capability |
|
Automated machine learning |
This feature enables non-experts to quickly create an effective machine learning model from data. |
|
Azure Machine Learning designer |
A graphical interface enabling no-code development of machine learning solutions. |
|
Data and compute management |
Cloud-based data storage and compute resources that professional data scientists can use to run data experiment code at scale. |
|
Pipelines |
Data scientists, software engineers, and IT operations professionals can define pipelines to orchestrate model training, deployment, and management tasks. |
As we dive deeper into this topic, it becomes evident that the fusion of these services will propel us closer to a future where technology and human ingenuity coalesce in ways that redefine progress. Stay tuned for our next blog post on #AI and if you have any question or suggestions, feel free to contact us.
