Organisations are now realising the benefits of data, thanks to the democratisation of machine learning which has put powerful tools in the hands of SMEs and large enterprises alike. What has changed the game for companies (small and large alike) is the access to algorithms and labelled data coupled with massive computing resources that helps teams train and deploy models on a large scale.
Today, machine learning is being provided as a service by multiple vendors, who provide compute and pre-trained models. In this article, we will look at how the Machine Learning as-a-service market is opening up access for small and medium enterprises to begin using artificial intelligence and scale according to their uses.
Big Tech Giants Lower The Barrier Of Entry for SMEs With MLaaS
Over the years, the existence of MLaaS market serves a bigger purpose for the scaling of small and medium enterprises. This means that organisations need not build an in-house internal machine learning team, or take the financial risk of establishing one only to find out that data cannot make the process more efficient.
While bigger companies can afford to take the risk that comes with building a sizable ML team, it is not possible for SMEs to do the same. At the stage of a startup, a bad investment towards the direction of the company can usually result in a failure state. This is due to the fact that hiring requirements for ML are extremely specialized. Moreover, expensive infrastructure is also required for the high amount of compute power required to train algorithms. This requires expensive computing machines, and infrastructural concerns like data pre-processing, model training and evaluation.
The general purpose nature of MLaaS allows organisations that have any need for ML to essentially plug and play, as ML suites today have APIs that can call different types of ML models for data analytics. Companies such as Google have offered pre-trained machine learning models via APIs that perform specific tasks. The user-friendly nature of these services also greatly reduce the barrier of entry for companies willing to use data analytics to superpower their processes.
Finding Solutions To Problems With Machine Learning
Companies today lack the infrastructure to store a massive amount of data. MLaaS offerings not only allow companies to look deeper into their data, but it also enables a culture of data collection. Storage and infrastructure needs are taken care of by the cloud storage products MLaaS vendors offer.
ML today is usually offered as a service and is incorporated into a cloud computing service. This includes multiple ‘cognitive’ algorithms such as facial recognition, Natural Language Processing and speech recognition, along with DNNs and CNNs along with data visualizations. While the market is not only developing at a fast pace, it is also an open one, as consumers have multiple choices when it comes to deciding the best MLaaS for their solution.
Due to the fact that data insights will provide an accurate insight into multiple processes, it will provide solutions to the company’s pain points when paired with a data-driven approach.
Laying The Foundation For An ML-Driven Future
The low barrier of entry and high effectiveness of solutions from vendors like Amazon, Google, Microsoft and Oracle leads to faster adoption of AI and takes the heavy lifting out of setting up their own infrastructure and building an in-house team.
Existing workers can be upskilled and reassigned to work on cloud solutions without sacrificing economical costs and while adding value to workers. Moreover, companies are allowed free cloud credit and trial for a specific period. If organisations find that these work for them, then they can continue using these solutions at a cheap rate, incentivising the use of AI.
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