Cloud machine learning platforms are making AI more affordable but also present challenges. ML is the ability of computers to learn without being explicitly programmed. It is growing rapidly and most work is done in the cloud. Key advantages of cloud ML include affordability, lower data storage costs, and tools that can simplify ML application development. However, skills shortages, vendor lock-in, and data integration can pose issues. Alternatives include open source tools or third-party predictive analytics solutions.

1. Cloud Machine Learning: Is it Right for
your Business?
Today cloud machine learning platforms are making artificial intelligence (AI) much more
inexpensive for businesses; however, before you invest in this technology, you must first
have a thorough understanding of the challenges that come with it. Let’s take a look at cloud
machine learning and the challenges that it brings in order to help you make an informed
decision about its use.
What is machine learning?
Machine learning (ML) is the ability of computer systems to learn things. In other words, the
computer can acquire new ‘knowledge’ by itself without it having to be programmed. ML is
the next stage of evolution for artificial intelligence – a computer that can make itself more
intelligent through its own processes and without human intervention. By applying complex,
pattern recognition algorithms to data, ML is able to produce highly accurate and much
sought-after predictions and decisions.

2. How popular is Machine Learning?
Machine learning is taking off in a big way. According to Deloitte Global, 2018 will see a
doubling of companies using the technology compared to last year, and this is likely to
double again by the end of the decade when the AI market, which includes machine learning,
will be worth £40 billion.
Currently, over six million developers are using machine learning with an addition 6 million
planning to begin using it by mid-2018. The vast majority of this work is taking place in the
cloud, which has the ideal environment for ML.
Use cases for Cloud Machine Learning
Cloud ML platforms, also known as ‘Machine Learning as a Service’ (MLaaS), are ideal for
applications that need to analyze Big Data – especially if the data and the platform are on the
same network. Cloud is an affordable and secure solution for mass data storage and its
scalability means MLaaS users can have all the resources they need on a pay as you go basis,
making it a cost-efficient platform.
Early adopters of this technology are predicted to be the financial and retail sectors, which are
expected to use ML to tackle fraud by analyzing their transaction data. Online retail
companies may also benefit from ML predictions about market trends and changing shopping
behaviors. This can improve their ability to stock better selling products, leading to increased
sales and a reduction in the amount of unsold stock left on the shelves. Additionally, cloud
ML is likely to be a fit for stock trading, business intelligence, IT security and for analyzing
risk.
There are, however, instances where cloud machine learning is not the best solution. This is
the case where data is remote and where instant intelligence decisions are required.
With large scale remote data transfer being an issue, this does cause problems for using ML
to make decisions for the control of Internet of Things (IoT) devices – which is unfortunate as
the two technologies are ideally suited.
Advantages of Cloud-Based Machine Learning
The keyadvantage of cloud machine learning is its affordability wherein MLaaSoffers
companies with a high-performance setupwhich would, for most, be too costly to create in-
house. Machine Learning apps demand significant processing resources and the systems and
power needed for this are not cheap. Public cloud, however, removes this barrier and can put
smaller companies on a level playing field with their much bigger competitors.
Less expensive data storage is another advantage. As organizations continue to accumulate
vast quantities of data, transferring that data to a public cloud is seen as a much more
economical solution than letting it clog up an in-house datacentre.

3. Of course, if data is transferred to the public cloud in order to reduce costs, it makes sense in
these situations to use MLaaS. This would shorten the amount of time it would take to
process the data and thus reduce costs even further whilst speeding up performance.
One other advantage of MLaaS is that the development tools it provides can make it simpler
to embed ML in applications. The lack of specialist ML app developers means there is a
shortage and those that are available are paid very highly. MLaaS development tools,
therefore, can be one way to keep these costs down and help overcome the skills gap.
Beyond cost, MLaaS has other benefits, too. Developments in machine learning mean that it
can be a tool that all people can use. Google, for instance, have introduced AutoML, which
aims to make artificial intelligence and ML accessible to all business professionals, even
those unable to code.
And in an age where speed is everything, it is now possible for companies to access cloud-
based ML solutions very quickly. By combining cloud services and automated ML, it is
possible to get companies set up in minutes, enabling them to create enterprise-class
predictive models and learn critical insights before their competitors.
Challenges of Cloud-Based Machine Learning
Cloud machine learning platforms do pose challenges for the organizations that wish to adopt
them. One of these is the above-mentioned skills shortage. According to Google, very few
businesses have access to the expertise needed to develop advanced machine learning
models. And developing these models is a time-intensive and highly complex process.
Another issue is vendor lock-in. Investing so much time and effort into developing your
cloud-based machine learning system, especially when you use the same vendor for your data
storage, can mean that migration becomes very problematic. This could mean you are at risk
from increased costs and other issues which the service provider may have.
Data Integration may also cause issues for ML. If you need data that comes from a range of
sources, gathering and transforming it into a usable format can be a challenge.
Cloud Machine Learning Alternatives
MLaaS platforms are not your only option. Many organizations use open source machine
learning tools or opt to buy-in predictive analytics and machine learning solutions from third-
party specialists. The software these provide is not specifically designed to be used in the
cloud and thus can also be deployed in-house.
Other applications are developing ML capabilities, too. You may discover that your security,
business and marketing intelligence apps, and your CRM, eCommerce and other software
already have ML capability. Consequently, you might not need to develop a specific ML app
for your own use.

4. Conclusion
Cloud-based machine learning offers many benefits to enterprises. Beyond the business
advantages of machine learning itself, moving to MLaaS can provide significant savings, cost
efficiencies and improved processing performance. However, there are issues, particularly the
skills shortage. However, with such demand and such well-paid posts to pick and choose
from, it won’t be long before Machine Learning becomes a magnet for IT talent.