In this article, I will elucidate about Azure Machine learning, why to choose Azure ML and Machine Learning Infrastructure requirements.
Azure, a most common and used word for cloud computing platform and services. Microsoft Azure is a growing collection integrated cloud services. In this post, I will not explain more about azure, instead, I will explore Azure Machine Learning.
Azure Machine Learning is a cloud based platform for designing a complete machine learning solutions where you can design, deploy, evaluate, automate, maintain and track the models. Machine Learning Studio is a cloud based, integrated development environments where you will have your workspace to conduct the ML experiments. The platform can handle all scenarios of Machine learning, starting from Classic ML to deep learning and is also suitable for supervised as well as unsupervised approach. You can go for code first approach with full flavor of writing Python or R code with the SDK. You have option to create workspace with notebooks (Jupiter Notebooks) taking full advantages of Python code likewise for RStudio.
Additionally, Azure ML gives you full capabilities to design machine learning solution with Low or No Code, using drag and drop Designer. With the use of Designer, a azure ML, you can add data source, do data transformation, train the model with different algorithms, as well do scoring and evaluation without writing any code with publish options as well. Isn’t it cool !!
The service also interoperates with popular deep learning and reinforcement open-source tools such as PyTorch, TensorFlow, scikit-learn, and Ray RLlib.
Why to choose Azure Machine Learning
So far, we have discussed about Traditional machine Learning Process which has been used over and over to create Training Models. These training models, obviously, is heart of ML solutions, however having these model is only part of complete machine learning solutions. Furthermore, there are additional Machine Learning infrastructure requirements for a complete solution.
To build a complete ML solution, there are several infrastructure components requirements. You need a place to Manage the data, then again we need an environment to sufficiently build Machine Learning Model, i.e. a workspace to build ML model and run experiments.
Soon after, we need to deploy the solution to use full capabilities of the application.
Next, we need to be able to manage the access of the solution. Like giving key so that only authorize users/clients can access our Machine learning application.
Finally, there should be a environment to maintain our ml solution as data, algorithm, scopes, and sometimes requirements gets change with time.
Therefore, we have these infrastructure requirements for complete ML solutions which can be achieved through a single platform, Azure ML, that’s why it is an ideal platform for Machine learning.
How are these resources being connected?
As depicted in the above picture, firstly, we use storage resources for ML data like blobs, tables, Azure SQL DB etc.
Then we fetch those data to integrated development environment for ML where we clean data, train with algorithm, evaluate the model.
Once our Model is ready with expected accuracy, we publish or deploy using web services.
Access control is another factor to consider to expose our solution/api to clients, so that, only authentic clients can use the solutions.
We have all these options available in a single place, Azure Machine Learning platform like storages services, Azure ML studio for developing Model. Similarly, App services, Compute instances, Kubernetes services, Containerize instances etc. options are available for deploying the solution with authentication and authorization inbuild.
Azure Machine Learning Options
Apart from Code first, Designer approaches, there are other productive options we have, for instance, Automated ML, which accelerated model development by suppressing manual iterative process.
Another one more paradigm is added, MLOps alike DevOps.
Machine Learning Operations (MLOps) is based on DevOps principles and practices that increase the efficiency of workflows. For example, continuous integration, delivery, and deployment. MLOps applies these principles to the machine learning process, with the goal of:
- Faster experimentation and development of models
- Faster deployment of models into production
- Quality assurance
Considering above all, Azure Machine Learning is an absolute platform for Machine Leaning with the copybook capabilities to fulfil complete requirements.
Cloud Jump, Have a good day!!!