12 min read

There has been a huge shift in the way that businesses build technology in recent years driven by a move towards cloud and microservices. Public cloud services like AWS, Microsoft Azure, and Google Cloud Platform are transforming the way companies of all sizes understand and use software. Not only do public cloud services reduce the resourcing costs associated with on site server resources, they also make it easier to leverage cutting edge technological innovations like machine learning and artificial intelligence. Cloud is giving rise to what’s known as ‘ Machine Learning as a Service’ – a trend that could prove to be transformative for organizations of all types and sizes.

According to a report published on Research and Markets, Machine Learning as a Service is set to face a compound annual growth rate (CAGR) of 49% between 2017 and 2023. The main drivers of this growth include the increased application of advanced analytics in manufacturing, the high volume of structured and unstructured data, and the integration of machine learning with big data.

Of course, with machine learning a relatively new area for many businesses, demand for MLaaS is ultimately self-fulfilling – if it’s there and people can see the benefits it can bring, demand is only going to continue.

But it’s important not to get fazed by the hype. Plenty of money will be spent on cloud based machine learning products that won’t help anyone but the tech giants who run the public clouds. With that in mind, let’s dive deeper into Machine Learning as a Service and what the biggest cloud vendors offer.

What does Machine Learning as a Service (MLaaS) mean?

Machine learning as a Service (MLaaS) is an array of services that provides machine learning tools to users. Businesses and developers can incorporate a machine learning model into their application without having to work on its implementation. These services range from data visualization, facial recognition, natural language processing, chatbots, predictive analytics and deep learning, among others.

Typically, for a given machine learning task, a user has to perform various steps. These steps include data preprocessing, feature identification, implementing the machine learning model, and training the model. MLaaS services simplify this process by only exposing a subset of the steps to the user while automatically managing the remaining steps. Some services can also provide 1-click mode, where the users does not have to perform any of the steps mentioned earlier.

What type of businesses can benefit from Machine Learning as a Service?

Large companies

Large companies can afford to hire expert machine learning engineers and data scientists, but they still have to build and manage their own custom machine learning model. This is time-intensive and complicated process. By leveraging MLaaS services these companies can use pre-trained machine learning models via APIs that perform specific tasks and save time.

Small and mid-sized businesses

Big companies can invest in their own machine learning solutions because they have the resources. For small and mid-sized businesses (SMBs), however, this simply isn’t the case. Fortunately, MLaaS changes all that and makes machine learning accessible to organizations with resource limitations.

By using MLaaS, businesses can leverage machine learning without the huge investment in infrastructure or talent. Whether it’s for smarter and more intelligent customer-facing apps, or improved operational intelligence and automation, this could bring huge gains for a reasonable amount of spending.

What types of roles will benefit from MLaaS?

Machine learning can contribute to any kind of app development provided you have data to train your app. However, adding AI features to your app is not easy. As a developer, you’ve to worry about a lot of other factors besides regular app development checklist, in order to make your app intelligent. Some of them are:

  • Data preprocessing
  • Model training
  • Model evaluation
  • Predictions
  • Expertise in data science

The development tools provided by MLaaS can simplify these tasks allowing you to easily embed machine learning in your applications. Developers can build quickly and efficiently with MLaaS offerings, because they have access to pre-built algorithms and models that would take them extensive resources to build otherwise.

MLaaS can also support data scientists and analysts. While most data scientists should have the necessary skills to build and train machine learning models from scratch, it can nevertheless still be a time consuming task. MLaaS can, as already mentioned, simplify the machine learning engineering process, which means data scientists can focus on optimizations that require more thought and expertise.

Top machine learning as a service (MLaaS) providers

Amazon Web Services ( AWS), Azure, and Google, all have MLaaS products in their cloud offerings. Let’s take a look at them. Google Cloud AI at a glance

Google Cloud AI

Google’s Cloud AI provides modern machine learning services. It consists of pre-trained models and a service to generate your own tailored models. The services provided are fast, scalable, and easy to use.

The following are the services that Google provides at an unprecedented scale and speed to your applications:

Cloud AutoML Beta

It is a suite of machine learning products, with the help of which developers with limited machine learning expertise can train high-quality models specific to their business needs. It provides you a simple GUI to train, evaluate, improve, and deploy models based on your own data.

Read also: AmoebaNets: Google’s new evolutionary AutoML

Google Cloud Machine Learning (ML) Engine

Google Cloud Machine Learning Engine is a service that offers training and prediction services to enable developers and data scientists to build superior machine learning models and deploy in production. You don’t have to worry about infrastructure and can instead focus on the model development and deployment. It offers two types of predictions:

  • Online prediction deploys ML models with serverless, fully managed hosting that responds in real time with high availability.
  • Batch predictions is cost-effective and provides unparalleled throughput for asynchronous applications.

Read also: Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine)

Google BigQuery

It is a cloud data warehouse for data analytics. It uses SQL and provides Java Database Connectivity (JDBC) and Open Database Connectivity (ODBC) drivers to make integration fast and easy. It provides benefits like auto scaling and high-performance streaming to load data. You can create amazing reports and dashboards using your favorite BI tool, like Tableau, MicroStrategy, Looker etc.

Read also: Getting started with Google Data Studio: An intuitive tool for visualizing BigQuery Data

Dialogflow Enterprise Edition

Dialogflow is an end-to-end, build-once deploy-everywhere development suite for creating conversational interfaces for websites, mobile applications, popular messaging platforms, and IoT devices. Dialogflow Enterprise Edition users have access to Google Cloud Support and a service level agreement (SLA) for production deployments.

Read also: Google launches the Enterprise edition of Dialogflow, its chatbot API

Cloud Speech-to-Text

Google Cloud Speech-to-Text allows you to convert speech to text by applying neural network models. 120 languages are supported by the API, which will help you extend your user base. It can process both real-time streaming and prerecorded audio.

Read also: Google announce the largest overhaul of their Cloud Speech-to-Text

Microsoft Azure AI at a glance

The Azure platform consists of various AI tools and services that can help you build smart applications. It provides Cognitive Services and Conversational AI with Bot tools, which facilitate building custom models with Azure Machine Learning for any scenario. You can run AI workloads anywhere at scale using its enterprise-grade AI infrastructure

The following are services provided by Azure AI to help you achieve maximum productivity and reliability:

Pre-built services

You need not be an expert in data science to make your systems more intelligent and engaging. The pre-built services come with high-quality RESTful intelligent APIs for the following:

  • Vision: Make your apps identify and analyze content within images and videos. Provides capabilities such as, image classification, optical character recognition in images, face detection, person identification, and emotion identification.
  • Speech: Integrate speech processing capabilities in your app or services such as, text-to-speech, speech-to-text, speaker recognition, and speech translation.
  • Language: Your application or service will understand meaning of the unstructured text or the intent behind a speaker’s utterances. It comes with capabilities such as, text sentiment analysis, key phrase extraction, automated and customizable text translation.
  • Knowledge: Create knowledge rich resources that can be integrated into apps and services. It provides features such as, QnA extraction from unstructured text, knowledge base creation from collections of Q&As, and semantic matching for knowledge bases.
  • Search: Using Search API you can find exactly what you are looking for across billions of web pages. It provides features like, ad-free, safe, location-aware web search, Bing visual search, custom search engine creation, and many more.

Custom services

Azure Machine Learning is a fully managed cloud service which helps you to easily prepare data, build, and train your own models:

  • You can rapidly prototype on your desktop, then scale up on VMs or scale out using Spark clusters.
  • You can manage model performance, identify the best model, and promote it using data-driven insight.
  • Deploy and manage your models everywhere.
  • Using Docker containers, you can deploy the models into production faster in the cloud, on-premises or at the edge.
  • Promote your best performing models into production and retrain them whenever necessary.

Read also: Microsoft supercharges its Azure AI platform with new features

AWS machine learning services at a glance

Machine learning services provided by AWS help developers to easily add intelligence to any application with pre-trained services. For training and inferencing, it offers a broad array of compute options with powerful GPU-based instances, compute and memory optimized instances, and even FPGAs. You will get to choose from a set of services for data analysis including data warehousing, business intelligence, batch processing, stream processing, and data workflow orchestration.

The following are the services provided by AWS:

AWS machine learning applications

  • Amazon Comprehend: This is a natural language processing (NLP) service that identifies relationships and finds insights in text using machine learning. It recognizes the language of the text and understands how positive or negative it is and extracts key phrases, places, people, brands, or events. It then analyzes text using tokenization and parts of speech, and automatically organizes a collection of text files by topic.
  • Amazon Lex: This service provides the same deep learning technologies used by Amazon Alexa to developers in helping them build sophisticated, natural language, conversational bots easily. It comes with advanced deep learning functionalities like, automatic speech recognition (ASR) and natural language understanding (NLU) to facilitate a more life like conversational interaction with the users.
  • Amazon Polly: This text-to-speech service produces speech that sounds like human voice using advanced deep learning technologies. It provides you dozens of life like voices across a variety of languages. You can simply select the ideal voice and build speech-enabled applications that work in many different countries.
  • Amazon Rekognition: This service can identify the objects, people, text, scenes, and activities, and any inappropriate content in an image or a video. It also provides highly accurate facial analysis and facial recognition on images and video.

Read also: AWS makes Amazon Rekognition, its image recognition AI, available for Asia-Pacific developers

AWS machine learning platforms

  • Amazon SageMaker: It is a platform that solves the complexities in the machine learning process, from building to deploying a model. It is a fully-managed platform that helps developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
  • AWS DeepLens: It is a fully programmable video camera, which comes with tutorials, code, and pre-trained models designed to expand deep learning skills. It provides you sample projects giving you practical and hands-on experience in deep learning in less than 10 minutes. Models trained in Amazon SageMaker can be sent to AWS DeepLens with just a few clicks from the AWS Management Console.
  • Amazon ML: This is a service that provides visualization tools and wizards that direct you to create a machine learning model without having to learn complex ML algorithms and technology. Using simple APIs it makes it easy for you to obtain predictions for your application. It is highly scalable and can generate billions of predictions daily, and serve those predictions in real-time and at high throughput

Read also: Amazon Sagemaker makes machine learning on the cloud easy.

Deep Learning on AWS

  • AWS Deep Learning AMIs: This provides the infrastructure and tools to accelerate deep learning in the cloud, at any scale. To train sophisticated, custom AI models, or to experiment with new algorithms you can quickly launch Amazon EC2 instances which are pre-installed in popular deep learning frameworks such as Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras.
  • Apache MXNet on AWS: This is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. It allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and mobile apps using Gluon. You can build linear regressions, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization, in just a few lines of Gluon code.
  • TensorFlow on AWS: You can quickly and easily get started with deep learning in the cloud using TensorFlow. AWS provides you a fully-managed TensorFlow experience with Amazon SageMaker. You can also use the AWS Deep Learning AMIs to build custom environment and workflow with TensorFlow and other popular frameworks such as Apache MXNet and Gluon, Caffe, Caffe2, Chainer, Torch, Keras, and Microsoft Cognitive Toolkit.

Conclusion

Machine learning and artificial intelligence can be expensive – skills and resources can cost a lot. For that reason, MLaaS is going to be a hugely influential development within cloud.

Yes, the range of services on offer are impressive from AWS, Azure and GCP, but it’s really the ease and convenience that is most remarkable. With these services it’s easy to set up and run machine learning algorithms that enhance business processes and operations, customer interactions and overall business strategy. You don’t need a PhD, and you don’t need to code algorithms from scratch. The MLaaS market will likely continue to grow as more companies realise the potential machine learning has on their business – however, whether anyone can deliver a better set of services than the established cloud providers remains to be seen.

Read Next

Predictive Analytics with AWS: A quick look at Amazon ML

Microsoft supercharges its Azure AI platform with new features

AmoebaNets: Google’s new evolutionary AutoML


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