Factors to Consider to Make a Choice Between Amazon SageMaker and Azure Machine Learning

Amazon Sagemaker and Microsoft Azure ML are currently the two big names on the machine language front. These two services possess all the modern functionalities that one can associate with machine language.

Notwithstanding the positives and downsides of these two services for machine learning, the fact remains that is difficult to make a choice between the two.

This post will compare the leading features of both the services to declare one as winner. Let’s take a look at Amazon SageMaker vs. Azure ML.

Also read – 6 Azure Cloud Adoption Pitfalls to avoid

Amazon SageMaker

In a nutshell, Amazon SageMaker can be defined as the machine learning language that is quick and easy to deploy. Nine out of ten developers adhere to this view. It can come in handy when one has to train and deploy the concept of machine learning in a business model within a short notice.

One of the highlights of the models of this service is that its modules can be used solitarily as well as with the other modules. Due to this versatile nature of its module, Amazon SageMaker has emerged in the market as one of the firm favorites for machine learning these days.

For those who prefer to store their training data in Amazon SE, this service brings Jupyter notebooks to the table. With these notebooks, one can visualize and browse business data from the Amazon S3 platform with ease.

Not only does it allow one to connect to one’s training data but also select or optimize a suitable framework and algorithm for one’s application. In case you find it difficult to choose the best algorithm, this machine learning system has the ten commonest forms of an algorithm to help you with the right choice.

As far as its training is concerned, you can make things simple and easy for yourself by tuning to its various training modes. It allows you the choice to pick and choose. Also, it takes care of accuracy while producing results. So, you do not need to worry about it.

Packed with A/B testing capabilities, Amazon SageMakes makes the deployment of a built and trained model as effortless as the word means. To deliver high availability as well as high performance, it makes use of the auto-scaling cluster of Amazon EC2. The best part about it is that it has a wider number of availability zones.

Thus, Amazon SageMaker eases the workload on developers by doing the bulk of the weight lifting involved in building, training and deploying the models of machine learning without using much time and effort.

Azure Machine Learning

Azure Machine Learning is a service that is geared towards diversifying the productive potential of developers for the building, training and the deployment of machine learning models. It is an innovative service from Microsoft that seeks to do this job in a time-bound manner.

Expert developers can use its power-packed features to gain an advantage. They can collaborate with machine learning operations and developer’s operations together. It is a trusted and innovative platform that leads to responsible AI.

The service by Microsoft is equally useful for beginners. With features like drag-and-drop and code-first designers, it is useful for developers of all skill levels.

The best part about this machine learning service is that it helps to manage a machine learning cycle comprehensively by integrating the machine learning operational model with the model of operation for developers.

Also read – GitOps for Machine Learning

It helps developers to build advanced AI solutions with a high level of interpretability and state-of-the-art fairness. By doing so, it provides developers with an advanced level of control and governance.

Lastly, its best-in-class support covers a wide range of languages and frameworks. These include Python and R, TensorFlow, PyTorch, ONNX, Kubeflow, and ML flow.

SageMaker vs Azure ML Studio: What to Choose?

1. Amazon SageMaker vs. Azure ML: Creating an environment

Amazon SageMaker provides you with access to the Jupyter notebook instance. In doing so, one needs to make use of cloud technology. The rationale behind it is to enable a user to access Amazon S3 and other similar services. Citizen scientists need to decide the S3 buckets they wish to access, the size of their cloud instance as well as some additional details. This can make them feel a little confusing, especially if they are using it for the first time.

In contrast, Azure ML Studio launches with a homepage. This allows users to skip various complexities in the decision making which is mandatory for Amazon SageMaker. Moreover, its appearance resembles that of an application that puts a developer at ease. Its fundamental layout is classified under several sections. These include projects, experiments, datasets, web services, trained models, and datasets.

2. SageMaker vs Azure Machine Learning: building and training a model

Amazon Sagemaker is packed with the ability to select the elements automatically. But Azure ML necessitates manual selection. The use of precise columns of split data modules and datasets is of paramount importance for splitting data using the Azure model.

With Sagemaker one needs to choose the framework and algorithm and program. On the other hand, much of the action of training takes place on the canvas with Azure ML studio. One needs to drag the necessary elements onto the canvas for training a module.

3. SageMaker vs. Azure Machine Learning: testing, scoring, and deploying a model

In the case of Amazon SageMaker, one needs to run a code in Jupyter. Following it, the next step is to write a Python code for drawing a comparison between the actual and predicted performance. Once this is done, one needs to end the session so prevent incurring further charges. This is not an automatic process, though. One needs to do this on one’s own.

In the case of Azure Studio, one needs to bring the score model and train model together on the canvas of the studio. These two models need to be connected to the canvas. Thereafter, the split data’s data output needs to connect to the module of the score model. Thereafter, one needs to select visualize after selecting the visualize button.

Once a data scientist obtains the results, deploying them is much easier in the case of Azure studio. As such, Azure emerges as the winner in the comparison between both the services.

Final Thoughts

The three main components of machine learning – creation, training, and deployment – have been used as the fundamental factor for SageMaker vs. Azure ML Studio comparison in this article. On all the three counts, Azure ML Studio leads the race against Amazon’s SageMaker. However, SageMaker and Azure ML are equally good in different situations. It is up to a user to decide which one meets their needs in the best possible manner.