Data science is a huge area of exploration covering subfields, such as machine learning, artificial intelligence, and natural language processing. All these subfields are tied together with the single most valuable item of the computer science domain – The Data. This data is as big in volume as it is big in value. The data projects require a huge amount of processing with precision and speed, and a large space of storage with integrity and completeness.

Traditionally, organizations have used high-powered servers to save and process their data. However, with greater awareness and clarity about the cloud, more and more data processing is being moved onto the cloud infrastructure. Cloud provides unparalleled benefits and ease of usage in terms of collecting, cleansing, analyzing, exploring, mining, visualizing, and storing the data. The machine learning and artificial intelligence projects require the setting up of the ML models, training the models with data, customizing the models, and automating the entire process of providing predictions and prescriptions to its users. It all happens at the accuracy and velocity needed by the users of the data. In case of a shortfall, cloud infrastructure can always be expanded elastically.

All major cloud service providers have dedicated services to cater to the requirements of a data science project. They offer additional services so that the data scientists and their teams can focus on their domain problems and leave the infrastructure management on the cloud service provider. The most significant benefits of using a cloud platform for a data science project are as follows.

• Lower cost: The cost of operation and storage are very low on the cloud. The project capacity and resources can grow according to the demand and need not be blocked upfront.
• Pre-integration with standard frameworks: A lot of cloud services come pre-integrated with the most widely used standard frameworks used in the area of machine learning and data science. It helps the data science project teams to quickly get started with their project execution without too many bottlenecks.
• Free Service: Most of the cloud service providers offer free-tier for usage and practice of machine learning projects so that users can get familiar and also try out practical data science projects on the cloud. It helps in creating a strong community of users who can provide support during the project execution.
• Security and Scalability: There is no limitation to the scalability of the services on the cloud. At the same time, with modern techniques and security principles, the cloud environments are equally safe as on-prem infrastructure.

All the top cloud services providers offer services for data science. We take a look at the various options available for getting started.

Amazon Web Services

AWS offers a host of services in the area of machine learning and artificial intelligence. AWS services cater to specific types of data, such as AWS Textract for processing handwritten and printed text from a document, and specific industries such as Amazon Comprehend Medical for the healthcare and Amazon Fraud Detector for the financial industry.

The Amazon service that leads and stands out from others is the Amazon SageMaker. The workflow of SageMaker is extremely natural and hence easy to understand and use by people with different levels of expertise and experience, such as Data Scientists, Data Engineers, DevOps Engineers, MLOps Engineers, Test Engineers, Business Analysts, and Business Users. The steps include setting up and preparing the environment, building and deploying the ML model, training and tuning the model with different types of data inputs, and running the model against the real dataset.

SageMaker is perfect for running an ML project because of the large choice of machine sizes and configurations offered by it, pre-installed libraries as well as runtime environments like TensorFlow, and integration with the standard Git codebase. Amazon provides pre-built highly optimized models and the users are billed only when their model starts training. One or more models can be hosted as within endpoint that can be invoked using a common code library. The entire process can be observed through the notification and monitoring services, such as AWS CloudWatch and AWS Kinesis, and raise alerts for the users.

Google Cloud Platform

GCP is another cloud service provider with viable enterprise-grade managed services in the area of data science, machine learning, and artificial intelligence. Google has a custom-developed Tensor Processing Unit (TPU) that is used to power its machine learning services giving it an edge over all other service providers. While running complex ML jobs, it increases the performance of the algorithm and reduces the time to accuracy factor during the training of neural network models. The Vertex AI service of GCP combines the ML with UI and API under a single flow and provides pre-integration with common open-source frameworks, such as TensorFlow and PyTorch.

Google Cloud AutoML service provides a no-code data-driven approach for building large ML solutions that are driven primarily by the Business teams. The trained models can be deployed on cloud or on-prem infrastructure with the help of RESTful APIs.

Azure Cloud

Azure is not far behind in the race of offering full managed services for ML projects. It has a Machine Learning and AI Platform that offers GUI options such as Azure ML Studio for developing the code, Automated ML service for a low-code approach, and supports SDKs of Python and R-language.

Azure has a great focus on integrating personal computer networks that can be used to push for vision-based and voice-based automation solutions. Azure Percept is a service in this area that is expected to change the way personal computing works. It would bring edge computing intelligence closer to the user.

Conclusion

ML and AI services are becoming exceedingly popular on the cloud as the technical and non-technical users are getting ready with their data and want to derive intelligence out of it without investing too much time or effort into the process. The cloud-based managed services offer common solutions to the data problems and help the users to quickly derive value from their data without a long wait for algorithm development, model training, and model deployment. Connect with us to discuss more on your data science projects and we can identify the best service available for your requirem