The benefits of using machine learning to streamline business processes are significant. It is a powerful tool for solving business problems. There are a lot of pre-trained AI models that can be used out of the box. But what if you need to develop your own machine learning models for specific needs?

The Era of Augmented Learning

In the old days, businesses that wanted to utilize artificial intelligence and machine learning had to establish their own teams of AI engineers and build everything from scratch. Today, that is certainly not the case. As mentioned before, pre-trained models are easy to find.

Even better, the best pre-trained models are available as open-source packages and services. Businesses have a clearer path to production and no longer have to figure out the most suitable way to get started.

However, pre-trained models have limitations. This is where retraining and augmented learning come in handy. Rather than using pre-trained models out of the box, there are ways to augment the ability of AI to perform tasks that are specific to what businesses need.

In automating mundane tasks, for instance, a machine learning model can be trained to calculate cost factors that are not present in other businesses. That level of customization opens up the use of machine learning to more use cases.

Also Read: AI Role & Importance in the App Development Process

High-Performance Computing to the Rescue

Another big barrier of developing a machine learning model from scratch is infrastructure. Even when retraining is the method of choice, developing a machine learning model still requires a considerable amount of computing resources.

That said, infrastructure is no longer an impossible barrier to overcome. This Enterprise HPC solution certainly makes spooling up and maintaining high-performance computing clusters incredibly easy. The infrastructure can be made very sustainable too.

This approach to high-performance computing means enterprises can use cost-efficient ARM-based hardware for heavy computing operations. Long-term maintenance is a lot easier too.

Practical Use of Machine Learning

That brings us to the actual use cases of machine learning models. From Natural Language Processing to advanced automation, the possibilities are endless. The most prominent use case for custom machine learning models is predictive maintenance.

Predictive maintenance utilizes machine learning to measure the lifespan of machines in a manufacturing line. Hundreds, even thousands, of sensors monitor data points in machines and the line itself. Through the use of machine learning, maintenance schedules can then be made predictive or preventative rather than reactive.

For small and medium enterprises, customer profiling is another good example. Small businesses can compete with the big boys by delivering a better, more personalized user experience. That is made possible with the help of insights generated by machine learning.

These technologies are more accessible than ever. If you are looking for ways to keep your business ahead of the market, artificial intelligence and machine learning are definitely the best technologies to incorporate into your business processes.

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