The concept of machine learning essentially aims to make computers learn as humans do. Since its inception close to 50 years ago, this technology has evolved giving us better, more refined ways to find useful patterns in large amounts of data. This is achieved by using algorithms which narrow down and specify common ‘if-then’ programs, resulting in more granular outcomes, widening the scope of its findings, and creating more possible outcomes.
In simple terms, machine learning is comprised of three steps:
A program is now unbeholden to a set of rules generated by an engineer. Instead, a machine is able to create a model of defining patterns uniquely to it after receiving a series of training examples. When all the mechanics are handled by the machine and engineers can simply focus on inputs and outputs, a near unlimited variety of application opportunities are created, from facial recognition to deep learning and (mostly) everything in between.
So why is machine learning getting loads of attention in recent times? Well, for one very simple yet logical reason: we’ve only recently developed the computational power required to process big data. It was one of those ideas which required technology of the future to be realised, like early dreams of space travel, breathing underwater and unlimited catalogues of cat images at the click of a button — what a time to be alive.
Data is the driver of machine learning. Think of it as its food — the more it devours the bigger, more complex and intuitive it becomes. Many of the world’s leading cloud providers now offer machine learning tools, including Microsoft, Amazon, Google and IBM. The main advantage these companies have over their competitors is their access and ability to generate their own big data, which places them in a completely different league compared to other smaller businesses or startups.
This has led these tech giants to provide machine learning as a service to businesses across the globe, allowing customers to pick and choose from a range of the microservices machine learning has made possible.
In a nutshell, Machine Learning as a Service (MLaaS) refers to a number of services which offer machine learning tools as a component of cloud computing services. MLaaS providers offer developers services that include predictive analytics, data transformations and visualizations, data modelling APIs, facial recognition, natural language processing and machine deep learning algorithms.
Apart from the numerous benefits MLaaS provides, one of the primary attractions of these services is the fact that businesses are able to get started quickly with ML. They don’t have to endure the laborious and tedious software installation processes, or provide their own servers as they would with most other cloud computing services. With MLaaS, the provider’s data centres handle the actual computation, so its ease of convenience at every turn for businesses.
By using AI software and services businesses are able to improve their product capabilities and offerings, make regular business operations more efficient, interaction with customers easier and use AI prediction capabilities to create more precise business strategies.
With MLaaS, developers get access to sophisticated pre-built models and algorithms which would otherwise take an immense amount of time, skill, and resources to build. This means they are able to devote more time to building and focusing on the important parts of each project.
Also, getting a team of engineers and developers with the required skill and knowledge to build machine learning models costs a lot, and there aren’t too many of them to choose from. Ultimately, the ease and the efficacy of MLaaS setups, with the obvious revenue spike they will provide, is a major allure for businesses.
The large cloud providers who are simultaneously creating and changing the game of MLaaS are Amazon, Microsoft, Google and IBM. Each provider offers different variations of machine learning services that come with their own unique challenges and rewards, so it’s best to define your business needs to ascertain which provider’s offerings suit you most.
Leaders in the SaaS field, Amazon Web Services are looking to achieve similar status in the MLaaS arena with their AWS Machine Learning solutions which guide users through creating machine learning models without having to learn algorithms themselves. After creating your models with the user-friendly visualization tools and wizards, predictions of your application are created by simple APIs without the user having to generate any code or manage any infrastructure.
Amazon Machine Learning offers a high level of automation which includes the ability to load data from multiple sources, including CSV files, Amazon Redshift, Amazon RDS, and more. Through a numerical and categorical sorting process, the service determines the accurate methods of data preprocessing entirely on its own.
Microsoft Azure offers a range of services, but we are focusing on its machine learning offering. Azure offers scalable machine learning for users of all sizes, suitable for AI beginners and pros alike. It offers a host of tools considered more flexible for templatized algorithms.
ML Studio is Azure’s primary MLaaS service that boasts a highly simple browser-based environment with drag-and-drop mechanisms which eliminates the need for coding. ML Studio provides users with a large variety of algorithms with over 100 methods for developers to use. ML Studio also provides users with access to the Cortina Intelligence Gallery, a community-based collection of machine learning.
Watson Machine Learning (WML) is a broad service provider powered by IBM’s Bluemix that includes scoring and training capabilities designed to address the needs of both developers and data scientists. The service handles deployment, operationalization and machine learning models which can create value for businesses. WML is also compatible with Jupyter notebooks in Python, Scala and R.
One of the main draws to this service is its visual modelling tools that assist users to rapidly identify patterns, gain valuable insights and ultimately enable them to make decisions faster.
Adding to its extensive SaaS range, Google has taken another giant step further into cloud service dominance by creating a sophisticated MLaaS platform. Building on its existing SaaS offerings, Google provides machine learning services for natural language processing and APIs for speech and translation, as well as for video and image recognition.
Google’s Cloud Machine Learning Engine boasts user-friendly ways to build machine learning models for data of any variety and size. Based on TensorFlow, the platform is integrated with all Google services with a priority focus on deep neural network tasks.
Most competitive businesses have already started to adopt AI in their operations, gaining a competitive edge as AI makes machine learning capabilities a hell of a lot easier. Through sophisticated cloud service offerings of the leaders in the game (Microsoft, Google, Amazon, IBM, etc.), businesses are now able to have the crucial benefits of machine learning outsourced as a service, without having to hire highly skilled AI developers and the huge price tag they come with.
The microservices which these large cloud services provide allow for easy setup, and the benefits are huge (if used correctly). Machine learning algorithms can enhance business processes and operations, customer interactions and the overall business strategy.
However, simply receiving the information machine learning reveals isn’t going to make your business the next major competitor to Amazon in terms of annual revenue. You need to know how to utilize the data correctly. Tangible reflections on your ROI will depend on a strategy implemented to back your findings.
Machine learning provides data based on many variables, and defining a manageable approach to incorporate this information in the best possible way to prove how valuable this new technology really is to your business.
This article was originally published by WeAreBrain.Terug