Machine Learning on AWS

“AWS is our ML platform of choice, unlocking new ways to deliver on our promise of being the world’s travel platform” – Matthew Fryer, Chief Data Science Officer, Expedia Group

Most of the businesses look at machine learning as if it is something unachievable because it is expensive and demands talent. Well, for some cases, it can be quite demanding but, with the trend of making everything-as-a-service, machine learning solutions are becoming easier to develop.  Any business or even an individual can jump-start a machine learning initiative without much investment. With the use of machine learning cloud services, you can start building your machine learning models.

Machine Learning as a Service

Machine learning as a Service (MLaaS) is an umbrella definition that covers most of the issues such as data reprocessing, model training, and model evaluation, with further prediction. Prediction results can be bridged with your internal IT infrastructure through REST APIs.

Let’s have a look at the most widely used ML cloud platform, Amazon Web Services (AWS), and the services that it provides. AWS is the most dominant public cloud provider. It offers the broadest range of machine learning services and the deepest set of Artificial Intelligence tools for businesses and individuals. These services are used in creating machine learning solutions with a faster pace. More than ten thousand customers, from the largest enterprises to the hottest startups, have chosen AWS Machine Learning services. This number of ML customers is way bigger than any other cloud services provider.

Amazon Machine Learning Services

Amazon Machine Learning services are available on two levels: predictive analytics with Amazon ML and the SageMaker tool for developers and data scientists. Since Amazon Machine Learning is no longer available for new customers, we will skip it and talk about Amazon SageMaker.

Amazon SageMaker

Amazon SageMaker provides tools for quick model building, training, and deployment. If you want to simplify data exploration and analysis without any server management, then Jupyter (an authoring notebook) is the best option provided by Amazon SageMaker. It covers the entire workflow of machine learning; labeling and preparing data, selecting an algorithm, making predictions, and finally taking action. SageMaker is an easy and low cost service for machine learning. Amazon also provides built-in algorithms such as Linear Learner, Factorization Machines, XGBoost, Image Classification etc.

Flexible Frameworks

You can use the built-in algorithms as well as you can create your own. You can also integrate SageMaker with other machine learning libraries like TensorFlow, PyTorch, and Apache MXNet.

Machine Learning APIs

By using Machine Learning APIs, users can easily implement machine learning whether adept with machine learning or not. Amazon also offers high-level APIs beside full development platform. Amazon Lex, Amazon Transcribe, Amazon Polly, Amazon Comprehend, and Amazon Translate are speech and text processing APIs. Amazon also provides APIs for image and video analysis known as Amazon Rekognition.

Compute Options in Amazon Machine Learning

You can use GPUs for compute-intensive deep learning, high-memory instance for running inference, and FPGAs for specialized hardware acceleration. AWS offers numbers of EC2 instances in accordance with the different machine learning requirements.

Other Important Aspects

When you perform machine learning, you also want security, data store, and analytics services. Amazon Web Services is a platform that provides all the requirements to support your machine learning workloads. It provides Amazon S3 and Amazon S3 Glacier for storage purpose, Amazon Redshift for analytics, as well as a secured service platform.

Want to start Machine Learning? All you have to do is register yourself in AWS and use Amazon Machine Learning services to avail the benefits.