Cloud-based machine learning platforms have become one of the most useful tools for enterprises and are being widely deployed across all sectors to carry out a multitude of tasks. Before businesses adopt Machine Learning (ML) it is important to consider not only its benefits but its challenges. Here, we examine both.
What is ML?
ML is the natural next step in the evolution of artificial intelligence (AI) and is the capacity of a computer system to learn new knowledge and skills independently; in other words, without them having to be programmed in. It is, undoubtedly, a revolutionary concept and one people are more akin to seeing in science fiction movies. However, today, it is a reality and computers can increase their intelligence through the processes of applying sophisticated pattern recognition algorithms to data. The benefit is that their learning enables them to make highly accurate predictions and decisions, adapt how they work to suit changing environments and acquire new skillsets.
According to Deloitte’s ‘State of AI and Intelligent Automation in Business Survey’ (2020), the use of machine learning has proliferated with 67% of respondents using ML in 2020 and 97% planning to use it by the end of this year. Similarly, deep learning, a specialised form of machine learning that uses neural networks based on the human brain, was used by 54% of respondents in 2020, with 95% planning to use it in 2021.
In 2020, ML was also the area of AI that companies invested most in, with over £30 billion spent globally on ML applications and platforms. Deployed across all sectors, its most popular uses were risk management, performance analysis and automation.
Machine Learning as a Service
In the cloud, organisations can now utilise ‘Machine Learning as a Service’ (MLaaS). These services are ideal for big data analytics and applications that rely on it, such as those used in stock trading, business intelligence, IT security and risk analysis. What’s more, with the cloud providing a secure, scalable and cost-effective solution for high-volume data storage, MLaaS adopters have all the resources they require on a pay-as-you-go basis.
MLaaS is not, however, the best solution for analysing data that is stored remotely or for companies that require AI to make instant decisions, as would be the case in autonomous vehicle management or for controlling Internet of Things (IoT) devices. The issue here is latency, though there are potential solutions in the pipeline.
Benefits of cloud-based ML
The chief advantage that cloud machine learning has over on-site ML platforms is affordability. ML platforms and applications require significant processing resources and for most enterprises, the cost of developing the necessary infrastructure in-house would be difficult to fund. MLaaS replaces investment expenditure with a monthly, pay-as-you-go model, giving companies cost-effective access to state-of-the-art technology that puts them on a level playing field with competitors.
Beyond affordability, cloud-based machine learning has other benefits. The development tools it provides, for example, make it easier for organisations to integrate ML with their other applications. With demand for ML expertise far outpacing the numbers of specialists working in the field, doing this in-house can be costly and require companies to wait for third-party consultants to become available. MLaaS development tools, therefore, can be one way to keep these costs down and help overcome the skills gap.
Developments in ML are also making it quicker to deploy and more accessible for non-specialists. In the cloud, it is now possible to set up ML solutions in minutes and by using tools like Google’s AutoML which professionals can use without coding skills, businesses can create enterprise-class predictive models and learn critical insights faster and easier than before.
The challenges of cloud-based ML
The lack of in-house expertise remains a key challenge for enterprises wanting to use cloud-based machine learning platforms, particularly in the development of advanced machine learning models, which is a complex, time-intensive process. However, open-source platforms are developing at pace and solutions are being developed around specific needs.
Another potential issue is with vendor lock-in, where a system is fully reliant on the services and infrastructure of a specific vendor. However, here too there are solutions. The use of containers makes it easier to migrate applications to different infrastructures and with multi-cloud becoming the cloud model of choice, there is a greater emphasis on cloud standardisation, with vendors moving in that direction to make themselves more attractive to enterprises.
Those who do use a multi-cloud approach, however, need to be aware of the issues that can be caused if an ML platform uses data stored in different formats across different clouds, as well as the potential impact of latency.
Cloud-based machine learning provides enterprises with a cost-effective and easily manageable way to take advantage of its advanced technology. Aside from the obvious business benefits of ML itself, cloud-based solutions like MLaaS deliver substantial savings, cost-efficiencies and enhanced processing performance. While there are challenges to deploying it, these are becoming easier to overcome and with 97% of companies expected to use it by the end of 2021, it’s certainly a technology that competitive enterprises shouldn’t overlook.
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