Azure ML Vs. AWS SageMaker Vs. Google Cloud ML in 2022

Amazon, Microsoft, and Google are very well known for their cloud services. In this article, the comparison is presented: Azure ML Vs. AWS SageMaker Vs. Google Cloud ML in 2022.

AWS SageMaker

Amazon Web Services offers a variety of Web services one of which is the AWS Sagemaker. AWS Sagemaker allows to build, train and deploy the machine learning algorithms. Following are some features:

Flexible Machine learning Software: AWS Sagemaker comes with plenty of different programming languages and different software frameworks to build, train and deploy the machine learning models. The 3 different ways to build ML models are:

• Built-in Algorithms
• Custom Algorithms
• Personally build Algorithms

Data processing and Exploration: to explore the data and processing of that data, Jupyter Notebook is the made available. It helps in creating, training, testing and deploying ML models.

Google Cloud Platform (GCP):

When compared to Amazon’s SageMaker, GCP ML Engine offers less flexibility for designing, training, and deploying machine learning models when it comes to supported software frameworks. The two main machine learning frameworks in GCP are TensorFlow and Scikit learn.

Jupyter Notebooks, unlike Amazon SageMaker, are not available in Google’s ML Engine. To access Jupyter Notebooks on the Google Cloud Platform, Datalab must be used. GCP divides data exploration, processing, and transformation into distinct divisions as distinct services. The Datalab service enables data exploration and processing; the DataPrep service enables exploration and the transformation of raw data into clean data for processing and analysis; and the DataFlow service enables the construction of batch and streaming data processing pipelines.

Microsoft Azure ML:

Azure AI, like Amazon’s SageMaker and Google’s ML Engine, is Microsoft’s response to Amazon and Google. Furthermore, Azure AI provides a variety of open and comprehensive platforms for developing, assessing, and deploying machine learning models, as well as many other capabilities that support multiple AI frameworks such as PyTorch, TensorFlow, Sci-kit Learn, Chainer, Caffe2, MxNet, and others. Furthermore, Azure Machine Learning Studio and Azure AI offer far more capabilities and features than competitors.

Some features of Azure ML are:

• Azure Machine Learning designer is a visual drag-and-drop UI for ML studio that offers access and controls to the platform’s functionalities. Here, you may alter our data, use ML algorithms, and deploy solutions on the server.
• Automated ML is a software development kit that enables no-code to low-code model training. Essentially, Automated ML augments ML studio by providing a high level of automation for common operations as well as assistance for data exploration, model development, and deployment. For training with Automated ML tools, Azure defines classification, regression, and time-series forecasting tasks.
• ML Studio completely integrates the Azure ML Python and R language SDKs.
• Support for machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn In addition, Azure provides compatibility between frameworks via the ONNX Runtime.
• Modular pipelines are included, allowing your team to build a bespoke data pipeline for your machine learning project.
• Data labelling project assistance, including data and team management, labelling progress, incomplete labelling tracking, and exporting labelled data tools.
• Customizable computing targets for model deployment are compatible with a wide range of cloud services, including Azure Kubernetes services, Container instances, and compute clusters.
• MLOps technology is available to manage, deploy, and monitor models inside automated workflows.

Azure ML Vs. AWS SageMaker Vs. Google Cloud ML in 2022:

Speech and Text Processing APIs:

Image Analysis APIs:

Video Analysis APIs: