Identify the product options

  • 5 minutes

AI is a broad classification of computing that allows a software system to perceive its environment and take action that maximizes its chance of successfully achieving its goals. A goal of AI is to create a software system that's able to adapt, or learn something on its own without being explicitly programmed to do it.

There are two basic approaches to AI. The first is to employ deep learning. A system that's modeled on the neural network of the human mind, enabling it to discover, learn, and grow through experience.

The second approach is machine learning, a data science technique that uses existing data to train a model, test it, and then apply the model to new data. The trained and tested model is then used to forecast future behaviors, outcomes, and trends.

Forecasts or predictions from machine learning can make apps and devices smarter. For example, when you shop online, machine learning powers product recommendation systems. These systems offer other products based on what you've bought and what other shoppers have bought who have purchased similar items in the past.

Machine learning is also used to detect credit card fraud by analyzing each new transaction and using what it has learned from analyzing millions of fraudulent transactions.

Virtually every device or software system that collects textual, visual, and audio data could feed a machine learning model that makes that device or software system smarter about how it functions in the future.

Azure product options

At a high level, there are three primary product offerings from Microsoft, each of which is designed for a specific audience and use case. Each option provides a diverse set of tools, services, and programmatic APIs. Let's take a look at the capabilities of these options, keeping in mind that we're only scratching the surface in this module.

Azure Machine Learning

Azure Machine Learning is a platform for making predictions. It consists of tools and services that allow you to connect to data to train and test models to find one that will most accurately predict a future result. After you've run experiments to test the model, you can deploy and use it in real time via a web API endpoint.

With Azure Machine Learning, you can:

  • Create a process that defines how to obtain data, how to handle missing or bad data, and how to split the data into either a training set or test set. The data is delivered to the training process.
  • Train and evaluate predictive models by using tools and programming languages familiar to data scientists.
  • Create pipelines that define where and when to run the compute-intensive experiments that are required to score the algorithms based on the training and test data.
  • Deploy the best-performing algorithm as an API to an endpoint so it can be consumed in real time by other applications.

Choose Azure Machine Learning when your data scientists need complete control over the design and training of an algorithm using your own data. The following video discusses the basic steps required to set up a machine learning system.

Azure Cognitive Services

Azure Cognitive Services provides prebuilt machine learning models that enable applications to see, hear, speak, understand, and even begin to reason. Use Azure Cognitive Services to solve general problems, such as analyzing text for emotional sentiment or analyzing images to recognize objects or faces. You don't need special machine learning or data science knowledge to use these services. Developers access Azure Cognitive Services via APIs and can easily include these features in just a few lines of code.

While Azure Machine Learning requires you to bring your own data and train models over that data. Azure Cognitive Services provides many pretrained models so that you can bring in your live data to get predictions on.

Azure Cognitive Services can be divided into the following categories:

  • Language services: Allow your apps to process natural language with prebuilt scripts, evaluate sentiment, and learn how to recognize what users want.
  • Speech services: Convert speech into text and text into natural-sounding speech. Translate from one language to another and enable speaker verification and recognition.
  • Vision services: Add recognition and identification capabilities when you're analyzing pictures, videos, and other visual content.
  • Decision services: Add personalized recommendations for each user that automatically improve each time they're used. Moderate content to monitor and remove offensive or risky content, and detect abnormalities in your time series data.

Azure Bot Service

Azure Bot Service and Bot Framework are platforms for creating virtual agents that understand and reply to questions just like a human. Azure Bot Service is a bit different from Azure Machine Learning and Azure Cognitive Services in that it has a specific use case. Namely, it creates a virtual agent that can intelligently communicate with humans. Behind the scenes, the bot you build uses other Azure services, such as Azure Cognitive Services, to understand what their human counterparts are asking for.

Bots can be used to shift simple, repetitive tasks, such as taking a dinner reservation or gathering profile information, on to automated systems that might no longer require direct human intervention. Users converse with a bot by using text, interactive cards, and speech. A bot interaction can be a quick question and answer, or it can be a sophisticated conversation that intelligently provides access to services.