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For years, IT and business professionals have been talking about the cloud and all the amazing things it enables, like speed to market, collaboration, and reduced capital expenditures. Now it’s time to discuss the cloud and machine learning (ML).
ML, a subset of artificial intelligence (AI), emulates human learning. It enables machines to improve their predictive capabilities until they can perform tasks independently without specific programming. And it’s becoming increasingly pervasive in business and our daily lives.
ML is behind many of the chatbots you converse with online. It makes “tagging” possible on social media ─ assigning names to photographed faces. It powers speech recognition in devices like Amazon Alexa and Google Home. It drives the recommendation engines used by Netflix and Spotify. It can be used for various applications, from medical image processing and real estate valuation to fraud detection and predictive analytics. It’s even used in IT security services.
Simply put, ML is helping companies drive rapid innovation and efficiencies and meet customer needs at an unprecedented pace and scale. So why aren’t all companies taking advantage of ML?
The problem many businesses face in pursuing ML projects is the lack of the necessary infrastructure. ML entails training algorithms, an extremely compute-intensive, time-consuming task. Training an algorithm using traditional CPU-based processors can takes days. Powerful graphics processing units (GPUs) can significantly reduce processing time but aren’t cheap. It’s difficult to justify their costs, mainly if ML projects are only conducted occasionally.
ML also entails using vast amounts of data, often in the petabyte range. In addition, training ML algorithms requires immediate access to new data. Both necessitate the need for tremendous storage capacity. But storage requirements for ML can also vary greatly, depending on the application and where it’s at in its lifecycle. Purchasing a lot of storage capacity that may not always be used can result in wasted storage and unnecessary expenditures.
Fortunately, the cloud can help businesses overcome these challenges.
The nature of the cloud and the resources it provides make it easier for companies to pursue ML projects.
Using public cloud resources ─ the Infrastructure-as-a-Service (IaaS) model ─ to power ML initiatives doesn’t require capital expenditures for the necessary infrastructure. The cloud services provider (CSP) is responsible for purchasing, maintaining, and ensuring the security of the infrastructure. Organizations can access powerful GPUs and flexible, scalable storage without investing in expensive hardware. Those organizations can trade capital expenses for operational expenses without the infrastructure to purchase and maintain.
There’s also cost-efficiency courtesy of the cloud’s elasticity, scalable resources, pay-per-use model, and inexpensive, often flexible data storage options. The cloud’s elasticity allows for customizing where and how much data is stored without requiring costly upgrades and system changes. In addition, data lakes housed in the cloud offer access to more extensive, better data for training without straining in-house resources.
For organizations just dabbling in ML to explore its potential, using the cloud can enable cost-effective testing and implementation of small projects, scaling them up or down as needs and demand change. Small data sets can be added, and you can quickly shift to larger data sets when the predictions become more accurate.
Another benefit of using the cloud for ML projects is that it offers high security. The data is stored in the CSP’s secure data center, and the CSP is responsible for the protection of the data center and infrastructure that powers its cloud services there. You’re still responsible for the security of your applications and data, but you don’t have to worry about building your security infrastructure.
Most CSPs also offer additional security services and/or security advisory services to protect your data further. In addition, many offer disaster recovery and backup services to protect your data in any circumstances that can put your data at risk of loss or corruption.
What it comes down to is that the barriers to pursuing ML app development — and taking advantage of the benefits of ML — have been lowered thanks to cloud resources.
ML's possibilities are vast and will likely continue introducing new capabilities, better ways of doing things, and much more. For businesses wishing to take advantage of some of the opportunities, the cloud is a vital tool for making it happen.
If you’re interested in learning what it might cost to move to the cloud, this eBook is an excellent place to start: On-Premises vs the Cloud: Comparing Costs and Making the Move.
Or contact US Signal and talk to one of our solution architects about your cloud interests. Call 866.2. SIGNAL or email [email protected].
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