The more cloud technology evolves, the more tasks and procedures can be offered by third parties “as a service,” alleviating the burden of organizations performing them in house with full-time employees.
Over the last decade, what began with software as a service (SaaS) has proliferated into more advanced offerings such as platform as a service (PaaS) and infrastructure as a service (IaaS). Now, even something as complex and data-intensive as machine learning can be offered as a service—if you have enough bandwidth.
Machine learning as a service (MLaaS) is an array of cloud-based services that provide machine learning to organizations on a subscription or pay-as-you-go basis. These services can include deep learning, predictive analytics, data visualization, application programming interfaces (APIs), and natural language processing. Machine learning capabilities can unlock many new possibilities for organizations, from improving customer service to streamlining and optimizing operations to creating new applications. MLaaS can, therefore, be an invaluable tool for organizations, enabling them to gain the benefits and advantages of machine learning without incurring the substantial infrastructural and personnel costs.
Many industries today are adopting MLaaS, including manufacturing, finance, insurance, healthcare, retail, transportation, and telecom. In fact, virtually all major cloud providers today have a MLaaS offering, including:
AWS Machine Learning
Amazon is probably the most revolutionary company in the SaaS realm, and one of, if not the, most dominant player in MLaaS.
Amazon Machine Learning enables users with minimal machine learning skills to use intuitive data visualization tools and wizards to create machine learning models, without having to learn complex algorithms. APIs then create predictions for your applications. Additionally, Amazon ML provides a number of built-in ML algorithms that developers can run on their data. Since Amazon ML is highly automated, users don’t have to create code or manage any infrastructure—making it well-suited for users with little to no machine learning expertise.
IBM Watson Machine Learning
Watson Machine Learning is a general service provider that runs on IBM’s Bluemix. It predominantly performs two different functions of machine learning: training (refining an algorithm so that Watson can ‘learn’ from a dataset) and scoring (predicting outcomes using a trained model). Watson is specifically designed to complement not only data scientists but developers building smart apps that can utilize the predictions made by the machine learning algorithms.
Watson also features a number of visual modeling tools that can help users identify patterns, glean profitable business insights, and expedite decision making. For smaller businesses, or businesses that don’t need to conduct a large amount of machine learning, Watson may be the best choice financially; it’s free to use as long as you conduct under 5,000 predictions and five compute hours per month.
Google Cloud Machine Learning Engine
Google’s Cloud Machine Learning Engine is part of Google Cloud, which includes the Google Cloud Platform public cloud infrastructure, as well as G Suite.
Cloud Machine Learning Engine enables developers and data scientists to build machine learning models, while also offering training and prediction services (batch and real-time). It also provides built-in tools to help you understand your models and effectively explain them to business users. Cloud Machine Learning Engine has been used by organizations to solve problems such as identifying clouds in satellite images, ensuring food safety, and responding significantly faster to customer emails.
Microsoft Azure Machine Learning Studio
Microsoft’s ML Studio offers scalable MLaaS for organizations of all sizes and is known for providing tools that are more flexible than other solutions for out-of-the-box algorithms. ML Studio comes with an enormous variety of algorithms at your disposal, with over 100 methods for developers to experiment with. Additionally, ML Studio includes access to Microsoft’s Cortana Intelligence Gallery, a community-based repository of ML solutions used by data scientists.
While the learning curve may be a little steep since almost all operations need to be completed manually, this eventually leads to a deeper understanding of all major techniques in the field. There is no coding necessary and the operations are simplified with a drag-and-drop interface.
Machine learning is optimized at the edge
The speed with which you utilize MLaaS matters greatly. In a business landscape that is becoming increasingly digital, the companies most likely to succeed are the ones that not only extract the most advantageous insights from their data, but do it faster and more nimbly than their peers.
Netrality’s interconnected colocation data centers are ideal for providing companies with MLaaS, and for hosting MLaaS providers. Connecting at the edge—the periphery of the internet as close to users as possible—empowers users with the fastest speeds, lowest latency, and highest processing power possible, optimizing machine learning.
MLaaS providers, such as Amazon Web Services, Microsoft Azure, IBM, and the Google Cloud Platform, can benefit from Netrality’s immediate proximity to multiple cable, ILEC and cellular networks in key markets. Built-in redundancy and direct on-ramps to the cloud allow these providers to confidently deliver services to their customers across the nation.
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