The unprecedented rise of cloud-based offerings, including SaaS and IaaS, has prompted the emergence of new competition in the market. This new addition — Machine Learning as a Service (MLaaS) — is one of the latest buzzwords in the IT industry. Machine learning implementation normally requires massive amounts of data and data scientists capable of identifying patterns in the data. Also, choosing a machine learning algorithm is often a hit-and-miss situation.
At the same time, there is a trade-off between certain aspects of the algorithms, such as predictive accuracy on new data, memory usage, the speed of training, and so on. Machine Learning as a Service provides a solution to all these problems.
Why MLaaS is here to stay
The cloud offers a suitable ML platform because of easy storage of large data volume, low deployment costs, and high computational performances. MLaaS providers use their own datacenters to handle the computations, relieving the customers from running their personal servers or installing their own software.
Moreover, MLaaS gives businesses access to ML tools without having to hire a data scientist. This way, they can assess and learn from data using algorithms and computers to gain insights about the data. What’s great about these services is their direct utilization from the cloud: no deployment of tools or installations are necessary.
Therefore, MLaaS provides the perfect opportunity for companies wanting to dip their toes in the ML trend without diving in right away. Also, integrating machine learning across various services via the cloud platform enables customers to lower the cost, reduce risks, and make more informed decisions.
Present state of MLaaS implementation
Even though it came under the spotlight recently, MLaaS is not a new entrant in the market. Corporations like IBM, Amazon, and Microsoft use this technology to provide customized services to clients. As a result, they allow organizations to make the most of customized machine learning algorithms without learning the comprehensive details about the technology.
This trend is changing, however, with new MLaaS companies providing the same benefits to small and midsized businesses. Due to the scarcity of qualified data scientists, more organizations are switching to MLaaS to fulfill their data requirements.
Why enterprises will lead the way
Enterprises will soon dominate the MLaaS sector for different reasons. The first is Big Data. Though open datasets are easier to access than before, larger organizations enjoy access to larger data volumes than small and midsized companies.
As they possess the required data, they can create ML algorithms and train them using the same data. This advantage puts them way ahead of startups and smaller competitors.
The competitive salaries offered by Microsoft, IBM, and Amazon is another factor. Startups have no way of matching to the same salary, and no longer is it acceptable to offer a stake in ownership.
Plus, these positions are mostly new, so even though several ML and data science courses are being offered to students, it takes years to get the education necessary for developing AI. Thus, the talent gap continues. But enterprises often possess the resources required to attract the talent they need. This enables them to build ML services.
A lot of businesses already use public cloud providers, so adding another microservice does not prove difficult. Therefore, MLaaS saves businesses energy, time, and resources.
What’s on the menu?
MLaaS makes high-performance ML infrastructure more affordable and accessible. Also, it helps organizations apply ML tools to cloud data without moving it. MLaaS providers offer development tools that simplify the process of embedding ML in applications while making sure proven ML technology solutions are available via fast and easy machine learning models.
Machine Learning as a Service also supports the deployment of ML models as web services, together with computational performance and high scalability.
Integration with various cloud services from the same provider is another major MLaaS benefit. The technology allows professionals without advanced degrees or coding expertise to use ML. Also, Google has recently announced AutoML to make AI more accessible to businesses.
Companies must keep in mind that the cost of switching from one vendor to another is high. To avoid this vendor lock-in, many avoid the uptake of MLaaS.
On top of that, integrating data from dissimilar sources and making it usable is a time-consuming and hard task. However, steps are being taken to improve the situation, and it’s only a matter of time before data scientists figure out how best to sort these existing MLaaS problems.
Taking a look at the future of MLaaS
Estimates suggest the MLaaS industry will have a 49 percent CAGR from 2017 to 2023. And it’s all thanks to the leading public cloud vendors such as Google, AWS, and Microsoft.
These pioneers are trying out different methods to feed large data volumes to enterprises so their ML algorithms can learn and gather knowledge from these datasets. While MLaaS is still in the early stages, enterprises may soon treat it as the leading ML/AI platform owing to numerous benefits.
Thanks to a fast-growing data volume, both researchers and companies want affordable and reliable techniques for extracting knowledge from it. MLaaS continues to offer a cost-effective system of engaging in the analytics necessary for success in this rapidly changing environment. The absence of skilled resources in ML tech may encourage companies to choose MLaaS.
Even though MLaaS has certain limitations, such as the lack of a trained network and data safety, it has the potential to grow into a much more sought-after service in the coming years. With time, it can even become the major driver behind the adoption of machine learning. This is mainly due to the ease with which MLaaS allows developers and businesses to benefit from the machine learning capabilities.
MLaaS and AI: Unbeatable combination
MLaaS will soon be the prominent enabler of AI adoption. Why? Because it’ll allow developers and businesses to benefit from ML capabilities. Not only will it lead to embedded artificial intelligence in business apps, but it will also permit organizations to use data in ways that would otherwise be impossible without hiring qualified AI developers.
They will soon be looking for more talent!
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