The 2 Best Machine Learning Services on Azure
Machine learning services have been in development for many years — and are rapidly becoming a part of the modern business landscape. The ability to process huge amounts of data and pick out valuable information such as trends, models and predictions is huge for organizations.
However, the issue that many companies face is that not many platforms offer an integrated machine learning (ML) solution that ties in with their existing business ecosystem. As a result, it can be expensive to integrate a new solution.
Microsoft's Azure platform includes many essential tools and services for everyday business needs — and machine learning and artificial intelligence (AI) are now available as well!
A Quick AI and ML Overview
AI and ML are exciting technologies that have the potential to enhance almost every aspect of our lives. AI and ML sort through large sections of data and draw conclusions. If you have more accurate data, then the conclusions drawn are likely to be more accurate, too.
It all depends on how the AI or ML technologies are trained. Examples of what ML can do is image recognition, written and spoken language recognition and generation, and video recognition. In order to have access to any of these AI/ML features, we will need to focus on Azure ML, and find out exactly what it offers us in terms of features and capabilities.
When we talk about Machine Learning in Azure, we can think about it as a programmatic method that builds a model based on many small steps. Each step of the way, the ML model needs to be fed what it should be learning from the data in order to fully use the inputs properly.
Once it has gone through a few rounds of this kind of training, it will finish with a result. This result will be compared to training data, and then the process will start again. This process runs until the results are consistent with what the training is looking for. You can see the online version of Microsoft Azure Machine Learning Studio.
We will look at three of the tools on Azure that you will need to know about in order to accomplish ML tasks by providing data, warehousing or compute.
If you are familiar with Visual Studio or SQL Server Management Studio, then you can think of this as a similar application in that it is a graphical designer that has been built to help people create ML experiments in a user-friendly environment. It also allows you to process ML reports and insights once you have run your experiments, which makes the data that you receive far more understandable.
2. Compute Custom ML
This provides a large-scale compute machine learning for custom experiments as well, which can be hosted on Azure. This is very important because you will often need to use far more resources in order to get an experiment complete within an acceptable time frame.
The shorter the time is between experiment and insight, the better you can adapt your strategies to get better results. Another benefit of having Azure provide the compute and storage in these instances can leave you free to explore other aspects of the project.
A machine learns by feedback. A typical training model will have researchers feeding thousands of samples into a model so that it understands what the baseline of the project is. One example would be training an AI to recognize human faces to differentiate them from beach balls.
After thousands of pictures have been identified as people, the ML experiment should have enough data to make the distinction between human face and beachball. You would then mix in some pictures of beach balls, and have the experiment give you the results of how many anomalies it found.
Microsoft uses a lot of AI technologies themselves, so the tools that they have made available on Azure are designed to offer features and processes that Microsoft uses themselves. Microsoft has all of the hardware and infrastructure to host these technologies such as Azure AI.
Azure AI houses some of the more exciting features such as cognitive services, data and bot services. Many of these technologies are already being used in companies, and Microsoft helps to make them more accessible.
It is important to note that cognitive services are highly customizable and can be developed for, much like a traditional application. This means that cognitive services can be quite code intensive for non-programming users, but for developers it is very easy to implement.
These tools will give you the freedom to provide your own code solutions to the environment so that you can train the bot services to provide feedback with actionable insights or action for you to further develop. There is a market place within the cognitive services portal where you will find many third-party developed apps.
Data (Visual, Audio, Text)
These services are able to interact with data types in ways that are not entirely dissimilar from how we humans do. It can process visual, audio and text data in an unformatted and unstructured form.
This means that raw data can potentially be worked with, saving lots of time. The main reason to use cognitive services is to draw some kind of automated conclusion from the data set.
Cognitive services can be used for:
Emotional Detection in video and images, and even text with Language Understanding Service (LUIS)
Azure Bot Services
Azure bots are able to understand human queries and then perform actions based on the user input and answers, also using LUIS. The bot service is an intelligence service that is powered by Microsoft Compute, on the hardware that Microsoft has developed for these tasks. Bot services are based on an array of specific questions and answers that are triggered when specific criteria is met in the chat session. This creates engaging user interactions that can steer a user to the correct channel for their queries.
If you want to look at how Microsoft creates bot services, take a look at this Microsoft Azure service for free in non-transactional environments. You don't need any coding experience. You can deploy a live chat bot if you want to test it out. The service is free because it is based on "no compute" and "no storage" technologies.
If you are planning on pursuing Microsoft Certified Azure Fundamentals (AZ-900) certification, you will need to understand the core services that Microsoft provides its customers on the Azure platform. Although people use ML and AI interchangeably, there are a few differences between the two in the Azure context.
Now that we have looked at the two best tools that are available on Azure for AI and ML, you should be comfortable with some of the key concepts that we have covered. Microsoft's Azure platform includes many essential tools and services for your everyday business needs – including AI integration with machine learning capabilities. If you want help getting started on your AZ-900 certification exam prep training, check out this training.
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