AWS Vs. Azure Vs. Google Cloud: Comparing Top Cloud Platforms For MLaaS

Machine Learning has become a pioneer in the data-driven world. Most enterprises use Machine Learning and Data Analytics to better understand their target audience, automate some of their production, create better products according to market demand, improve business performance, etc. All of these things can lead to increased profitability and give them an edge over their competitors.

However, Machine Learning requires infrastructure, the right set of expertise, tools and technologies to build, test, and implement your ML algorithms from scratch and deploy. While many SMEs might not be familiar with the demands and requirements of a machine learning model deployment.

What is Machine Learning (ML) Deployment?

ML deployment means Machine-learning (ML) models are deployed to a production environment to generate business value. However, deploying to a production environment is a time-consuming process. At the same time, the successful deployment of models requires domain-specific knowledge to overcome new engineering and operational challenges.

ML deployment incorporates an ML model to a production environment keeping in mind the scalability, portability, and performance aspects of the model developed. It can be done by making use of the available methods like Batch Prediction or On-Demand Prediction and so on.

How Machine Learning Models are Deployed?

The general deployment for machine learning models will mainly consist of the below steps:

• Create and develop a model in a training environment
• Clean the code and test ready for deployment
• Prepare code for container deployment
• Plan and prepare for continuous monitoring and maintenance once machine learning deployment is successful

Getting machine learning applications into production can be tedious and hard.

To overcome these hassles, cloud computing comes to the rescue. Many cloud computing platforms are offering strong machine learning services and functionalities.

Microsoft, Google, and Amazon are most often named as Top MLaaS Providers. These cloud service providers help giants to SMEs enterprises that don’t want to build, test, and implement their machine learning algorithms from scratch. These companies can focus on their core business and obtain value addition from Machine Learning without needing to become experts.

Machine Learning Platforms and Frameworks

However, choosing a specific cloud platform requires due diligence and comparing each platform to fully understand its capabilities and differences.

Amazon Web Services (AWS)

AWS offers a wide range of tools and services for the ML objectives capable of leveraging the huge cloud compute and data capacity.

Amazon SageMaker

Amazon Web Services provides the Amazon SageMaker platform offering tools that enable developers and data scientists to easily build, train and deploy machine learning models at any scale. For instance, to simplify data exploration and analysis without server management hassle, it provides Jupyter, an authoring notebook.

The Built-in SageMaker methods provide the data scientists the ability to customize, add their methods, datasets and run models leveraging its deployment features. It also facilitates integration with TensorFlow, Keras, Gluon, Torch, MXNet, and many other machine learning tools and libraries.

Following are different ways in which ML models are trained and deployed using SageMaker:

• Create and deploy ML model inside SageMaker using SageMaker’s built-in algorithm containers
• Create your model and then deploy using SageMaker’s built-in algorithm containers (Bring Your Own Model type )
• Create a model outside SageMaker, bring your container inside SageMaker, and deploy it for usage

Azure ML (Azure ML Studio)

Microsoft Azure ML Studio is a web interface that provides a wide range of services to quickly build, train, and deploy machine learning models. It offers a drag-and-drop interface with simple modules for performing common functions like accessing data, cleansing data, scoring, testing models and deployment, etc.

It is designed in such a way that inexperienced developers and data scientists can easily train and deploy models without the complexity of managing cloud instances, Python coding, and Jupyter Notebooks. This accelerates the development and deployment of machine learning models.

The built-in modules help to preprocess the data to build and train ML/DL models using machine learning and deep learning algorithms such as recommendation systems, computer vision, anomaly detection, text analytics, etc.

Following are different ways in which ML models are trained and deployed using Azure ML:

• Development with a choice of tools: Work with Jupyter Notebook, drag-and-drop designer, and automated machine learning
• Create and deploy models at scale: using automated and reproducible machine learning workflows
• Innovate responsibly with a rich set of built-in responsible capabilities to help you understand, protect and control data, models, and processes
• Build your way along with the best-in-class support for the open-source frameworks and languages such as MLflow, Kubeflow, ONNX, PyTorch, TensorFlow and Python