Machine Learning as a Service (MLaaS)
Prerequisite – Machine Learning (ML)
Now a days in this digital world of technology where each day we are listening about Machine Learning (ML) and Artificial Intelligence (AI). Like Artificial Intelligence as a Service (AIaaS) which has already entered into technology market like that Machine Learning as a Service (MLaaS) also present in the tech industry. In this Recently Forbes has predicted that the global Machine Learning market would go from $7.3B to $30.6B in next 4 years. To do this, Data Scientists & ML personnel have to keep on building models to go on with dynamic business needs of customers. But just building models is not sufficient at all, they also have to keep in view of many processes like
- Examine new ideas
- Maintain these models
- Enhance performance
It is nearly impossible task for data scientists to meet the demands with a certain rate. With help of Machine Learning as a Service they can handle these problems comfortably.
Machine Learning as a Service (MLaaS) :
Machine Learning as a Service (MLaaS) are group of services that provide Machine Learning (ML) tools as a constituent of cloud computing services. Service provider in Machine Learning as a Service provide tools such as deep learning, data visualization, predictive analysis, recognitions etc. It can also play a role in eradicating infrastructural concerns like model training & evaluation, pre processing etc.
How actually MLaaS works ?
- MLaaS is manufactured on cloud architect, consists of containers and kubernetes that lay a Foundation for Functions as a service (FaaS) and Software as a Service (SaaS) solution. Instead of providing whole set of tool company can provide a finely tuned ML model from just on service.
- Its algorithms are used to search for patterns in data as mathematical model are made through these models which help in predicting new data.
- The platform offers both pattern recognition & probabilistic reasoning that created different method from which customized workflow can be done as per company needs.
What can we expect from MLaaS Platform
- Time Saver : MLaaS provide data scientists the path to begin quickly with ML without doing some tiresome software installation process.
- Easy to Use : MLaaS proposes the provider’s data center where they can directly calculate actual computation which is convenient in every stage of business.
- Access to ML Tools : MLaaS providers offer APIs for healthcare, face recognition, sentiment analysis etc. For business they also provide data visualization and predictive analysis.
- Data Management : Several companies save their data in cloud storage from their own storage which need to be effectively organized. Here MLaaS offer cloud storage and provide method for management of these data in several ML experiments e.g. Data Pipelining.
Benifits of using MLaaS :
- With the help of MLaaS developer can easily get access to prebuilt model & algorithms which was time consuming i.e now they can focus, examine and improve the different parts of project.
- Also by using AI, service business can improve their potentials of offering to customers , regular effective interaction with customers can provide more accurate business strategies.
- Bringing out the results that we want from MLaaS setup & keeping an eye on revenue spike can act as a boon in business, otherwise getting a team of developers and engineer with vital skill and knowledge which are limited in nos. and costly.
- MLaaS platform can take duty for management & storing data of small and medium sized business which are highly deficit to store massive volume of data.
Some popular MLaaS in the market :
- Amazon ML : Amazon Web Service (AWS) ML provide high level of automation which have capability including , CSV files, load data from multiple servers, Amazon Redshift, Amazon RDS. The service can determine precious method of data processing through some sorting processes.
- Azure ML : Microsoft Azure Studios offers ML which is suitable for both AI beginners & pros. The MLaaS provide simple browser based environment which provides drag & drop mechanism, rather than coding. It also provide large amount of algorithms with numerous method which can be used by developers.
- IBM Watson ML : Watson Machine learning is a wide range of service provider by IBM’s Bluemix which is addressed for needs of both Data Scientists and developers. The service with its own visualizing model tools aims to rapidly identify them, get valuable insights which enable them to take decisions faster in business.
- Google Cloud Machine Learning Engine : It is a user friendly way to builds ML models for data of any size. It also provide ML service for natural language processing for not image & video recognition but also for speech & translation.
When not to use MLaaS :
With a lot of advantages and application, services MLaaS always tries to change our life by providing better services day by day and making our life more easier. Still, organizations need to avoid MLaaS at some points i.e
- If the data need to be secured and on-premise we should prevent using MLaaS.
- If the data need high level of optimization in future then MLaaS may not be required.
- If you need to optimize service cost of complex algorithms then we may take infrastructure on premises.
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