What is machine learning?
This technological world is shifting towards technologies like artificial intelligence, IoT, machine learning and deep learning. The fourth industrial revolution which has combined the concept of a machine with the power of intelligence is providing our successful results in terms of modern automated chatbots, voice and text-enabled searches, face recognition, and many more valuable products.
Well, these products are the outcome of the complex machine learning and deep learning algorithms used to develop such data-oriented models. After the introduction of cloud computing where a user can leverage virtual resources such as infrastructure, storage, networking, testing platform, etc., the development of such applications has increased. Azure machine learning is one such cloud-enabled service that is being used for training, deploying, managing, and automating machine learning (ML) models at a massive scale. Let’s explore it in this article.
What is Azure Public cloud?
Microsoft Azure, which is also known as Windows Azure, is Microsoft’s public cloud computing platform developed for providing varieties of cloud services. These services include the platform for computation, networking, storage, analytics, IoT, and Artificial Intelligence application development. One can easily go with the service of their choice and create a scalable application, test, and deploy it. And, the best part is, you will only pay for what you use.
What is Machine Learning?
Machine learning technology refers to the various techniques used to train existing data for fetching valuable insights from it. By leveraging ML models and provided data, we can predict future behaviors, trends, and outcomes. The ML algorithms have the ability to learn without explicitly programmed.
With ML-based predictions, you can make your application or devices much smarter serving you the best from it. For example, you may have observed the recommendations while shopping online. Yes, these recommendation engines are empowered by complex ML algorithms. One more example of ML can be seen in making transactions through credit or debit cards where these ML models compare the information through a transaction database for identifying fraudulent transactions.
Well, you must be wondering how these models can provide accurate outcomes. Let’s understand it with the Azure ML services which will help you provide a crystal clear view of ML model development and deployment.
What is the Azure Machine Learning service?
Azure machine learning services allow you to create, test, manage, deploy, migrate, or monitor ML models in a scalable cloud-based environment. Azure machine learning services support thousands of open-source packages available in Python such as TensorFlow and Matplotlib. The supported ML tools make it easy to explore, transform, create, and test data models. E.g., Azure Machine Learning for Visual Studio Code and Jupyter notebooks. Azure ML services assist us with automated model generation and tuning to develop efficient and accurate models.
The best part about Azure ML service - you can train your model over the local machine and then deploy it on the cloud. Azure offers computing services like Azure Databricks, Azure Machine Learning Compute, and advanced hyperparameter tuning services allowing you to create better models.
Once you have created the right model, it’s time to deploy it over containers like Docker which makes it easy to deploy it to Azure Kubernetes Service or Azure Container Instances.
You can take care of deployed models and monitor various executions to get the best outcome. Once it is deployed, you will get asynchronous predictions (real-time) on a massive amount of data.
The advanced machine learning pipelines make a collaborative environment in all steps, including data preparation for deployment.
What you can do with the Azure Machine learning Service?
Azure machine learning service has the potential to auto-train and autotunes a model. The Azure machine learning software development kit (SDK) available for Python and open-source packages allows us to create and train accurate deep learning and ML models in an Azure machine learning service workspace. Various ML components can be accessed through Python packages like Scikit-learn, PyTorch, MXNet, TensorFlow, Microsoft Cognitive Toolkit (CNTK), etc.
After creating the model, you need to create a container like Docker which should be tested locally. Once tested successfully, you can deploy it as a web service using Azure Kubernetes service or Azure Container service. Now, the Azure portal or Azure Machine Learning SDK for Python will assist you to manage the deployed web model. It will help you to evaluate model metrics, redeployment of modified versions, and model tracking simultaneously.
