Data science cloud services

Cloud platforms for data science: how AWS, Azure, Google Cloud and IBM differ from each other

Artificial intelligence (AI) may have begun its journey earlier than cloud computing, but it is the cloud computing platforms that have improved AI, Machine Learning (ML) and data science at large. The proliferation of cloud computing and delivery model evolution from Infrastructure as a Service (IaaS) to Platform as a Service (PaaS) and Software as a Service (SaaS) has led to data science being effectively run on cloud.

Data science as a field is varied and usually involves working with unstructured data, implementation of machine learning concepts, and generating insights. The most critical of these workflows is machine learning since training ML models is time-consuming and resource intensive. But with cloud computing, data science in the cloud is easier with dedicated tools and services from cloud vendors like Amazon, Microsoft, Google, and IBM.

The idea of taking data science projects to the cloud comes with advantages like ability to scale, less maintenance from the user side, and access to all the latest tools. Some of the most commonly used cloud-based platforms are Amazon Web Services, Google Cloud Platform, Microsoft Azure, and IBM Watson. All of these platforms have their own strength and different essentially boils down to the kind of ML or data science application you are building.

AWS

AWS is the most prominent cloud platform for machine learning, artificial intelligence and data science. The cloud platform claims to offer the broadest and complete set of tools for data science. However, the most commonly used tool in AWS’ arsenal is SageMaker.

Amazon SageMaker

SageMaker is a fully-managed machine learning platform for data scientists and developers. It runs on Elastic Compute Cloud (EC2), and enables users to build ML models, organise data, and even scale operations. The AWS marketplace offers models to use, without requiring users to start from scratch. Some of the ML applications include speech recognition, computer vision, and recommendation.

Amazon Rekognition

Amazon Rekognition is a computer vision service that helps with the development process for image and video recognition applications. It is an essential tool for business needs such as object detection and classification, face recognition, facial analysis, and inappropriate scene detection. With Amazon Rekognition, users can start training their models with as little as 30 images and scale them as per their need.

Amazon Lex

Amazon Lex is an API designed to integrate chatbots into applications. The API is capable of recognising spoken and written text, and contains deep learning-based natural language processing (NLP). Lex supports chatbot deployment for services like Slack, Facebook Messenger, and Twilio.

Google Cloud Platform

Google Cloud has become one of the top choices among data scientists and it provides machine learning and AI services on two levels. There is Cloud AutoML for beginners and Google Cloud Machine Learning Engine for experienced data professionals.

Google Cloud AutoML

Google Cloud AutoML is a cloud-based machine learning platform built for inexperienced users. With this tool, users can upload their datasets, train models, and deploy them directly. AutoML also integrates with all Google services and can be accessed via a graphical user interface. Some of the services available include training models on structured data, image and video processing services, natural language processing and translation engine.

Google Cloud Machine Learning Engine

The Google Cloud ML Engine is designed for data scientists and allows them to run machine learning predictions at scale. The Google Cloud ML Engine can be used to train a complex model by leveraging GPU and Tensor Processing Unit (TPU) infrastructure. With Cloud ML, users can automate all monitoring and resource provisioning processes. It can also be tuned to influence the accuracy of predictions.

Microsoft Azure

Like Google, the machine learning offerings from Microsoft can also be classified into two main categories – Azure Machine Learning Services and Bot Service. They are also considered to be more flexible in terms of deployment. Here is a look at these two services from the Redmond-based software giant.

Azure Machine Learning (Azure ML) Services

Azure Machine Learning (Azure ML) Services offers a huge library of pre-trained, pre-packaged machine learning algorithms. It also acts as an environment for data scientists and ML engineers to implement these algorithms and see their inference in real-world applications. The Azure ML Services includes offerings such as Python packages, experimentation, model management, workbench, and visual studio tools for AI.

Azure Bot Service framework

Like Amazon Lex, Azure Bot Service framework acts as an environment for building, deploying, and testing bots using different programming languages. Microsoft offers a total of five pre-defined bot templates, which means you don’t need machine learning methods. In order to build bots with Azure, you can use Node.js and .NET technologies. These bots can be deployed across services like Skype, Bing, Office 365 email, Slack, Facebook Messenger, Twilio, and Telegram.

IBM

IBM is one of those services offering tools to support the entire data science lifecycle. From preparing and exploring the data to deploying and monitoring the models, IBM’s cloud platform acts as a complete package. Here is how IBM Watson Studio, IBM Cloud Pak for Data, and IBM SPSS Modeler help data scientists.

IBM Watson Studio

IBM Watson Studio allows users to build, run and manage AI models at scale across any cloud. The product is actually part of IBM Cloud Pak for Data, the main data and AI platform. The Watson Studio offers a flexible architecture that supports open-source frameworks like PyTorch, TensorFlow, and scikit-learn.

IBM Cloud Pak for Data

IBM Cloud Pak for Data helps users collect, explore and analyse the data across any cloud and also accelerates insights with an integrated modern cloud data warehouse. IBM says Cloud Pack delivers a “data fabric to connect and access siloed data on premises or across multiple clouds without moving it.”

IBM SPSS Modeler

The SPSS Modeler from IBM is one of the leading visual data science and ML solutions helping enterprises speed up operational tasks for data scientists. It is used for data preparation and discovery, predictive analysis, model management and deployment, and monetise data assets. The SPSS Modeler is also available within IBM Cloud Pak for Data and takes advantage of open source-based innovation.

Conclusion

The difference between various data science solutions in the cloud boils down to algorithms, features, pricing, and programming languages. When choosing between these four major cloud vendors for data science, it is important to define your machine learning goal and then choose the service that offers the most comprehensive tools at a reasonable price to accomplish those goals.

2048 1196 Editorial Staff

Editorial Staff

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