The development of a product into full-fledged service/s on cloud has seen the rise of new services such as Platform as a service (PaaS), Infrastructure as a service (IaaS) and Software as a service (SaaS). Their growth as a market has led to a battle in the cloud space market. Joining these cloud-based services and slowly opening up another competition is Machine Learning as a service (MLaaS). The growing trend of shifting data storage to cloud, maintaining it and deriving the best insights from it has found an ally in MLaaS which provides these solutions at a reduced cost.
What Is MLaaS:
Machine learning as a service (MLaaS) is an array of services that provide machine learning tools as part of cloud computing services. MLaaS helps clients benefit from machine learning without the cognate cost, time and risk of establishing an inhouse internal machine learning team. Infrastructural concerns such as data pre-processing, model training, model evaluation, and ultimately, predictions, can be mitigated through MLaaS.
Service providers offer tools such as predictive analytics and deep learning, APIs, data visualisation, natural language processing and more. The computation aspect is handled by the service provider’s data centers.
How MLaaS Functions:
Simply put, MLaaS is a set of services that offer ready-made, slightly generic machine learning tools that can be adapted by any organisation as a part of their working needs. These services range from data visualisation, a slew of application programming interfaces, facial recognition, natural language processing, predictive analytics and deep learning, among others. The MLaaS algorithms are used to find pattern in data. Mathematical models are built using these patterns and the models are used to make predictions using new data.
The key is in the fact that the users (in this case, organisations who purchase MLaaS) do not need to handle the actual computation. The providers’ data centres manage it remotely. MLaaS is also the only full-stack AI platform that consolidates systems ranging from mobile application, enterprise information, industrial automation and control, as well as advanced sensors such as LiDar, among others.
MLaaS is a platform that provides both pattern recognition and probabilistic reasoning. This provides thorough and sound ML solution that provides the flexibility of using different methods to create customised workflow specifically to meet the company’s needs.
MLaaS are supported by algorithms such as convolutional neural network (CNN), deep neural networks (DNN), Bayesian networks, probabilistic graphical models, Restricted Boltzmann machine (RBM) and pattern recognition, among others.
Many cloud providers including Microsoft, Amazon and IBM, among others, offer MLaaS tools.
Key Players In The Market:
Machine learning in the automation of services is not a new concept. But renewed interest in the area over the past decade and the progressive transition of all services to cloud makes MLaaS a relevant tool of the future.
Amazon’s Amazon ML, Microsoft’s Azure ML, IBM’s Watson and Google Cloud ML are some of the leading providers of MLaaS services.
Following are some of the MLaaS services offered by the key players in the market:
- Natural language processing: Amazon Comprehend, Azure Web Language Model API, Google Cloud Natural Language API
- Speech recognition: Amazon Transcribe, Azure Custom Speech Service, Google Dialogflow Enterprise Edition
- Computer vision: Amazon Rekognition, Azure Custom Vision Service, Google Cloud Vision API
- AI platforms: Amazon Sagemaker, Azure Machine Learning Studio, Google Cloud Machine Learning Engine
Who Uses MLaaS:
MLaaS has already seen uses across various industries. It is being used in processes such as risk analytics, fraud detection, manufacturing, supply chain optimisation, network analytics, marketing, advertising, predictive maintenance, and inventory management optimisation, among others. The application spans across various industries as well, such as healthcare, banking, financial services and insurance (BFSI), transportation, retail, manufacturing, and telecom, among others.
How Does MLaaS Benefit SMBs:
Most MLaaS service providers offer ascendable and customised technologies to companies and provides them with the advantage of choosing specific services that are ideal for them. The biggest benefit that MLaaS offer is the freedom from the burden of building in-house infrastructure from scratch. Many companies, especially small and medium sized businesses (SMBs), lack the infrastructure to store massive volumes of data and the internal resources to manage them. The investment in storage facilities for all this data is also a costly affair. This is where the MLaaS platform takes responsibility for management and storage of data.
With the assistance of ML technology and computing capacity provided by MLaaS, companies can now have a competitive edge in the market. They can venture into similar services provided by their larger and established competitors without having to worry about sophisticated and large scale ML and data needs.
Also, MLaaS provides the company with faster, and sometimes previously invisible insights and enables better and quick decision making.
Future Of MLaaS:
With data and its engagement going the cloud way, MLaaS will help revolutionise a paradigm of machine learning and will create a synergised result. According to a study, MLaaS market will witness a 49 percent growth during the forecast period 2017-2023.
Another area that MLaaS could drive innovation is IoT. According to a study , over 20 billion units of equipment (excluding PCs, tablets and smartphones) will form the IoT by 2020. With MLaaS already having the capacity to integrate with various kinds of sensors, MLaaS could play a key role in that area as well.
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An unapologetic movie buff with a special admiration for Marlon Brando and Stanley Kubrick, Jeevan is a post graduation student in Journalism and Mass Communication. He hopes to make an impact with his uncompromising reportage some day.