Democratization of cloud computing for AI/ML learning

There is a way that small businesses can now access cloud computing for AI and ML resources at lower costs

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Cloud computing

not anymore only a tech giant’s affair


In recent times, businesses are utilizing AI/ML to their fullest potential since they can now more easily access and develop them on the cloud. Many enterprises are leveraging AI & ML on cloud to benefit from better integrated technology, vast storage, robust data processing and better prediction- all the while saving time and money. Applications range from simple anomaly detection to enhanced Business Intelligence applications and Predictive/Prescriptive analytics solutions Business Intelligence and customer support. With the top cloud computing companies bringing strategic investments in AI/ML as part of their strategic organizational goals, it has also gradually led to the democratization of AI/ML. This means that more and more small businesses can now access cloud for AI & ML resources at lower costs. Here’s how:

 Cloud empowering AI & ML application

The cloud technologies have made it possible to access its intelligent capabilities without having to be an expert in data science. It is not always necessary to master AI/ML theory to use ML frameworks such as TensorFlow, which can be run on companies' hardware. One does not need expert skills to train or deploy the ML models. Given that dedicated special-purpose hardware may not be necessary, it saves the costs for labor and infrastructure development. It saves companies from complex in-house models or computational clusters. What’s more, cloud is facilitating a pay per use model - a highly economical solution.


 Streamlined and precision-based offerings

Cloud-based platforms provide a variety of general and specialized services, allowing for easy use. One example is general-purpose services from Google Cloud ML Engine that help in coding based on TensorFlow and Python libraries. On the other hand, Amazon Rekognition offers a specialized image-recognition service that is designed to run with a single command. Specialised services are useful in case of specific requirements such as video analysis while general-purpose services allow writing custom code within them and adapting as needed. 

 Reduced processing time


Companies such as Google are creating hardware that can optimize machine learning tasks. The quantum of processing power is determined by the workload. The more processing power, the more expensive it is. That is why hardware is a crucial component for machine learning workloads. Powerful Graphics Processing Units (GPUs) are applied for AI/ML workloads as these can be faster on training a model to recognize patterns. Also, Cloud supports GPUs to train machine learning models and then deploy the results to one’s own devices. This reduces amount of data to be transferred to the cloud thereby cutting down costs and any backlogs in results.

Anomaly detection

When there is a huge amount of data, you will want to be careful not to lose sight of it. Machine learning has been instrumental in identifying unusual events from the vast data which could be anything such as user load that is more than anticipated or CPU utilization that could be related to future events.


Despite the above advantages there are a few challenges for enterprises to adopt cloud-based AI/ML in full force. One is that behavior of applications may not always be predictable in the test environment. Machine learning applications alter the behavior in production and help to adapt to new situations. To be successful at cloud computing for machine learning services, there needs to be scaling up of provisioning and machine learning models.

One might also want to be watchful of how much cloud service one is using. As it is enticing and viable to use AI services to increase workload capacity, it is likely that one ends up paying more than what one needs. Utilizing too little of the provision might, however, cause the application to crash as the number of users increases. Forecasting models might therefore be necessary to estimate the growth trajectories, upgrading infrastructure or sticking to the existing model.

All in all, cloud platforms have been creating more efficient building blocks for automation thereby enabling better deployment and learning of AI & ML. The tools have been sought-after. As a result, enterprises are sure to have them and still pay wisely, making it an affordable and viable option.

The article has been written by Jaimy Thomas, Head of Service Delivery, Experion Technologies