Sagemaker Comparisons

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Google Colab

Google Colaboratory is a cloud service that can be used for free of cost, provided by Google. It supports free GPU and is based on the Google Jupyter Notebooks environment. It provides a platform for anyone to develop deep learning applications using commonly used libraries such as PyTorch, TensorFlow, and Keras. It provides a way for your machine to not carry the load of heavy workout of your ML operations. The interface is pretty simple and you can connect it to your google drive. It is the best option for someone who is already using Google services, which is a big amount of the population.

Azure Notebook

Azure notebooks by Microsoft is very similar to Colab in terms of functionality. Both platforms have a cloud sharing functionality available for free. Azure Notebooks wins in terms of speed and is much better than Colab in this regard. It has a memory of 4 gigabytes. Azure Notebooks creates a collection of related notebooks called Libraries. These libraries are the size of each data file as less than 100 megabytes. Azure Notebooks supports the programming languages of both Python and R.

Amazon Sagemaker

Amazon SageMaker notebook runs on the Jupyter Notebook App. It is responsible to create and manage Jupyter notebooks that can further be used to process data and further train and deploy ML models. For the training and deployment of the models, it provides APIs. Amazon SageMaker provides a console that lets the user use the console UI to start model training or deploy a model. It allows for the easy integration of ML models in applications. For people who deploy models in AWS, this is an added benefit.

IBM Dataplatform Notebook

IBM introduced the Watson Data Platform and Data Science Experience (DSX) back in 2016 with support for open-source options. These options included Apache Spark, R, Python, Scala, and Jupyter notebooks. Colab needs data science to be fone on its own public cloud. IBM supports containerization because it encourages customers to be able to analyze data and build, deploy, and run models anywhere, including rival public clouds. DataPlatform Notebooks supports languages of R, Python, and Scala. DSX users can use open source libraries including Spark MLlib, TensorFlow, Caffe, Keras, and MXNet.