Note: If you are already familiar with machine learning you can skip this post and jump directly to the Creating a Machine Learning Web Service post by Diego Poza, which explains how you can use Azure Machine Learning with a specific example.
Machine learning is a science that allows computer systems to independently learn and improve based on past experiences or human input. It might sound like a new technique, but the reality is that some of our most common interactions with our apps and the Internet are driven by automatic suggestions or recommendations, and some companies even make decisions using predictions based on past data and machine learning algorithms.
This technology comes in handy specially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates (websites clicks, credit card transactions, GPS trails, social media interactions, etc.), and it’s becoming a challenge to process all the valuable information and use it in a meaningful way. This is where rule-based algorithms fall short: machine learning algorithms use all the collected, “past” data to learn patterns and predict results (insights) that helps make better business decisions.
Let’s take a look at these examples of machine learning. You may be familiar with some of them:
- Online movie recommendation on Netflix, based on several indicators like recently watched, ratings, search results, movies similarities, etc. (see here)
- Spam filtering, which uses text classification techniques to move potentially harmful emails to your Junk folder.
- Credit scoring, which helps banks decide whether or not to grant loans to customers based on credit history, historical loan applications, customers’ data, etc.
- Google’s self-driving cars, which use Computer vision, image processing and machine learning algorithms to learn from actual drivers’ behavior.
As seen in the examples above, machine learning is a useful technique to build models from historical (or current) data, in order to forecast future events with an acceptable level of reliability. This general concept is known as Predictive analytics, and to get more accuracy in the analysis you can also combine machine learning with other techniques such as data mining or statistical modeling.
In the next section, we will see how we can use machine learning in the real world, without the need to build a large infrastructure and to avoid reinventing the wheel.
What is Azure Machine Learning?
Azure Machine Learning is a cloud-based predictive analytics service for solving machine learning problems. It provides visual and collaborative tools to create predictive models that can be published as ready-to-consume web services, without worrying about the hardware or the VMs that perform the calculations.
Azure Machine Learning Studio
You can create predictive analysis models in the Azure ML Studio, a collaborative, drag-and-drop tool to manage Experiments, which basically consists of datasets and algorithms to analyze the data, “train” the model and evaluate how well the model is able to predict the values. All of this can be done with no programming because it provides a large library of state of the art Machine Learning algorithms and modules, as well as a gallery of experiments authored by the community and ready-to-consume web services from Microsoft Azure Marketplace that can be purchased.
- What is Azure Machine Learning Studio?
Understand more about the Azure Machine Learning Studio workspace and what you can do with it.
- Machine learning algorithm cheat sheet
Investigate some of the state of the art machine learning algorithms and to help you choose the right algorithm for your predictive analytics solution. There are three main categories of machine learning algorithm: supervised learning, unsupervised learning, and reinforcement learning. The Azure Machine Learning library contains algorithms of the first two, so it might worth a look.
- Azure Machine Learning Studio site
Get started, read additional documentation and watch webinars about how to create your first experiment in the Azure Machine Learning Studio tool.