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AWS and Google AI: An Analytical Comparison

Machine learning as a service (MLaaS) is a range of services providing artificial intelligence (AI) tools as a part of cloud-based computing services. On a global scale, MLaaS market is rapidly evolving with a projected CAGR of 41.2% (2017-2023). This growth is fuelled by integration of advanced analytics in manufacturing processes, great volumes of structured and random data and the merger of machine learning with big data. On an organizational level, it is the small and medium businesses that are increasingly adopting machine learning service. The machine learning algorithms provide real time data and help in predicting the future. This allows manufacturers to streamline their supply chain by accurately forecasting product demands. To leverage the tech optimally, an analytical understanding of two of the biggest services providers, that is, Amazon Web Services (AWS) and Google AI is essential.

Amazon Web Services (AWS) AI

AWS is a major player in the MLaaS market and the biggest entity in the Cloud computing arena. It offers machine learning services that help developers apply AI to any application with pre-set tools. You get to choose from an array of services such as business intelligence, GPU-based instances, compute and memory optimized instances, batch processing, stream processing etc. AWS AI services broadly fall into three categories.

Machine Learning Services

  • Amazon Comprehend: This natural language processing (NLP) service recognizes the text language and understands its tonality, extracts key phrases, places, people, names or events. The text is then analysed and based on this, it automatically creates a topical compilation of text files.
  • Amazon Rekognition: It helps in identification of objects, people, text, visuals and actions or any inappropriate content in image or video format. It is capable of advanced facial analysis and recognition based on images and videos.
  • Amazon Lex: This service offers the same deep learning technologies to developers that Amazon employs in Alexa. It helps them create advanced, natural language, interactive bots with ease.
  • Amazon Polly: Amazon Polly is a text-to-voice service and it produces speech that resembles actual human voice by employing advanced deep learning tech. This facilitates the creation of various realistic voices in a number of languages.

AWS Machine Learning Platforms

  • Amazon Sagemaker: It is a completely managed platform helping the users to quickly build, train and activate artificial intelligence tools irrespective of the scale.
  • AWS DeepLens: This is an entirely customizable video camera offering you practical and real-time deep learning experience, and is offered with tutorials, code, and pre-set models.
  • Amazon ML: Amazon ML is an extremely scalable service offering visualization tools that help a user create a machine learning model without a need to learn intricate algorithms or technology.

AWS Deep Learning Services

  • AWS Deep Learning AMIs: It provides the required setup and tools to speed up Cloud-based deep learning at any scale.
  • Apache MXNet: It is an easy-to-use machine learning API that allows developers to start Cloud-based dep learning irrespective of their skill levels. A user can build AI based tools for object or speech recognition and customization.
  • TensorFlow: AWS AI offers you a fully-managed TensorFlow service with Amazon SageMaker.

Google Cloud AI

Google prides itself in being an AI first platform. Google’s Cloud AI offers latest machine learning services comprising of pre-trained models and an option to generate your own customized models. Its services are swift, scalable and easy to use. These include the following.

Cloud AutoML Beta

It comprises machine learning products that can train high-quality models tailored to specific business requirements. It provides a simple GUI to train, analyse, modify and deploy models derived from custom data.

Google Cloud Machine Learning (ML) Engine

This service offers training and forecasting services that help developers and data scientists build advanced AI models and deploy them. It provides two options.

  1. Online prediction: It deploys server-less machine learning models with end-to-end managed hosting that has a real-time response with very high availability.
  2. Batch predictions: It is a more economical option and provides unmatched throughput for asynchronous applications.

Google BigQuery

Google BigQuery is a Cloud data storage enabling data analytics. It is based on SQL and offers Java Database Connectivity (JDBC), and Open Database Connectivity (ODBC) making integration easier and faster. Using it, the data scientists can create fantastic reports and dashboards using popular BI tools such as Tableau, MicroStrategy, Looker etc.

Dialogflow Enterprise Edition

It is a complete, multi-utility development suite that helps create conversational interfaces for websites, mobile apps, messaging services and IoT gadgets. Dialogflow Enterprise Edition gives users access to Google Cloud Support and a Service Level Agreement (SLA) for production deployments.

Google Cloud Speech-to-Text

This tool facilitates conversion of speech into text by leveraging neural network models. Its API supports 120 different languages that help in reaching out a vast user demographics. It is capable of processing real-time streaming or pre-recorded audio data.

Which one is better?

Both AWS and GCP are highly effective MLaaS providers offering affordable, scalable and highly customizable Cloud-based services. Both come up with a wide range of services that eliminate the need for cost-intensive machine learning infrastructure and offer equal opportunities to small and medium enterprises to leverage the Cloud-based AI services for their specific requirements. Experiences may vary depending upon the user’s needs, but it is evident that both AWS AI and GCP AI are extremely helpful in widespread proliferation of accessible AI tools that will further expand the IoT vision in the years ahead!

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