- The application of Virtual Personal Assistants like Siri, Google Assistant and Cortana is making devices smarter and improving users’ search experience.
- “By 2025, the vehicle will be sophisticated enough to configure itself to a driver and other occupants. It will be able to learn, heal, drive and socialize with other vehicles and its surrounding environment.” IBM Automotive 2025: Industry without borders survey.
- Facebook can now automatically detect faces with new Face Recognition Capabilities. Thus, saving users from manually tagging friends.
The instances above explain how Machine Learning (ML) is becoming a part of our everyday lives.
A technique of data science, machine learning allows computer systems to make use of existing data to forecast future behaviors, trends and outcomes, without being explicitly programmed for it.
Through data learning patterns, ML based applications help devices perform better. From suggesting products based on user’s search patterns or detecting frauds during credit card transactions, it has various uses and benefits.
The increasing need for predictive analytics and forecasting requires development of new machine learning apps. Microsoft Azure Machine Learning helps organizations and individuals build and deploy predictive and analytics solutions quickly and easily.
Azure Machine Learning – A quick overview
Azure Machine Learning is a predictive analytics service based on cloud. It provides ready-to-use library of algorithms. These can be used to create predictive models on any internet-connected PC, followed by quick deployment as analytics solutions.
Below is a basic workflow of Azure Machine Learning:
Anyone who is new to machine learning can easily start building models by using built-in examples from Cortana Intelligence Gallery.
The Microsoft azure machine learning by Azure apart from providing tools to build solutions, also provides a fully-managed service that can be used to deploy the predictive models as consumable web services.
Predictive Analytics with the help of algorithms, study the trends in current set of data and predicts future events based on that.
Building complete machine learning solutions using Azure Machine Learning in the cloud
Azure Machine Learning is a comprehensive solution that helps developers build predictive analytics models in cloud. It includes built-in support and hundreds of packages for custom code. People without prior data science background can also build models through easy drag-and-drop interface. It is built upon the trusted and proven Microsoft solutions like Bing and Xbox.
Azure Marketplace can be used by data scientists to create custom web services, publish APIs and even charge for their usage.
Following is a short video that explains the power of Azure cloud machine learning.
It helps build predictive solutions through tools like:
- Cortana Intelligence Gallery
- Machine Learning Studio
Cortana Intelligence Gallery
Cortana Intelligence Gallery is a site for discovering and sharing solutions that have been built with Cortana Intelligence Suite.
It includes a variety of resources that can be used to build custom analytics solutions:
- Experiments – It contains all experiments developed in the Azure Machine Learning Studio, from specific machine learning techniques to complex problems.
- Jupyter Notebooks – Through this, users can maintain a single documentation for code and data visualizations.
- Tutorials – Various tutorials are available to help users understand different concepts and algorithms related to machine learning.
- Solutions – Get access to preconfigured solutions, reference architectures, and designs to help users quickly build solutions.
The gallery further groups together the resources in various ways through Collections and Industries (Retail, Manufacturing, Banking and Healthcare). Users can discover, add and deploy solutions through the Gallery.
Machine Learning Studio
Azure Machine Learning Studio is a collaborative tool with drag-and-drop facility to build, test and deploy predictive analytics solutions. It also publishes models as web services to be easily consumed. Following is an overview of Machine Learning Studio capabilities by Microsoft:
Azure Machine Learning Studio with the help of an interactive and visual workspace helps users to easily build, test and even iterate on predictive analytics models. The easy drag-and-drop interface facilitates quick experiments.
The users can even edit their existing experiments, save their copy and run them again if required.
Once the user has full confidence over his experiment, he can go ahead and publish the predictive experiment as a web service.
An Experiment includes datasets which can be connected to construct a predictive analysis model.
The best part is, users do not need to get involved in programming complications. They can simply connect datasets and modules to construct their predictive analysis model.
Deploying models as a web service
Any predictive solution to be deployed as an operationalized web service goes through the stages shown in graphics below:
Azure Machine Learning Studio allows users to deploy their predictive experiment as a:
- New or
- Classic Web Service
Users can send data to their predictive model and get predictions in return. The studio also permits the user to retrain or update his published models.
Azure Machine Learning in the cloud is the best way for both beginners and experienced data scientists to start their experiments and build smart predictive models.
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Priya an ambivert by nature, believes in giving shape to her ideas through her write ups. She is an intellectual person who loves exploring and researching about new things. In her free times she loves reading novels along with some soft music.