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Machine Learning has become more ingrained into our everyday lives, from smart assistants to detecting credit card fraud. In this post, we’ll talk about what machine learning is and some specific announcements related to Microsoft’s Azure Machine Learning tools.
Let’s start with what machine learning is. Machine learning is a type of artificial intelligence (AI), where computers take data and “teach” themselves through algorithms and statistical analysis to be more accurate in their analysis of that data and any pattern predictions that arise as a result.
Machine learning is similar to data mining, where programs search through data to look for patterns and adjust program actions accordingly. You probably already know one real-world example of machine learning: Relevant internet advertising based on your shopping or browsing history. On the outside, it looks pretty cool (or creepy, depending on how you look at it) to see relevant items based on your previous interactions with a particular website, but the technology working behind the scenes is relatively simple: The software is just using patterns in the user’s data to make assumptions about their behavior and interests.
Just because the technology is simple doesn’t mean it doesn’t have an enormous impact on the way businesses operate. Data has now become an extremely valuable commodity–the more a company knows about a user/consumer, the more they can provide relevant services or products.
So, how does this relate to Azure? Azure Machine Learning (now called Azure Machine Learning Studio) is simply one platform upon which to build your AI/predictive models. Rather than build your own machine learning tools on your own infrastructure, you can simply use Azure’s services to build what you need with fewer costs.
Microsoft recently held its annual Build conference in early May, where it made several announcements relating to AML. Perhaps the biggest announcement was the preview of Azure Machine Learning Hardware Accelerated Models. The models use a specific programmable chip that can significantly accelerate performance for different algorithms, especially those used for machine learning.
According to Microsoft, these acceleration models can actually be a good bit faster than GPU acceleration, so the Hardware Accelerated Models have the potential to create an AI infrastructure that’s extremely fast. Additional bonus: Microsoft says these models can also offer a price-performance ratio that’s five times better than without them.
Other new features of Azure Machine Learning Studio include deployment of models to Azure Container Instances, Azure Kubernetes Service and Azure Batch AI, for training and scoring purposes; and integration with IoT so that machine learning can be done at the edge and not just in the cloud.
Pretty exciting stuff! There are many possibilities for machine learning, and the Azure platform is just one platform upon which to take advantage. Read our FAQ to learn more about predictive analytics and machine learning, and here’s where you can learn more about Azure Machine Learning Studio.
Struggling to manage your Azure infrastructure, including the instances that run your predictive models? We can help! Learn more about our managed Azure services.