Machine learning is taking the world by storm. And with the introduction of the Internet of Things, the value of machine learning has escalated to a new horizon. Think about it, tech experts such as Mark Zuckerberg and Sundar Pichai are always bragging about machine learning and how it can alter our lifestyle. But time and again, we come to one last question – How will it affect our businesses?
Today, we will understand what is machine learning and the requirements to capitalize this innovative technology.
What is Machine Learning?
Machine learning is a revolution in the tech world. With this technology, machines can solve problems without being explicitly programmed. The machine will have the ability to learn different things, we recognizing what is happening around it.
The operational algorithm of ML differs from traditional devices. Devices with machine learning system have the ability to make decisions by analyzing the historical data. Today ML used in businesses include.
- Google maps predicting the road congestion on your route.
- Email filters analyzing messages as either spam or not.
- Facebook’s facial recognition system that identifies people in a picture.
- Netflix recommending its users the next movie to watch.
Let us take the example of Facebook’s facial recognition. The technology capitalizes machine learning to detect the user in the photograph. The technology can detect the user with 98% accuracy; while the FBI’s facial recognition technology is only 85% accurate.
Shifting to an ML Algorithm
The operating algorithm of Machine Learning can be daunting for many. While we try to adopt this technology at its best, we fail to analyze how it can affect our business. We need to capitalize the technology depending upon our requirement. First, we must understand the problem we are trying to solve and select the solution that caters our needs. So before considering machine learning, here are some questions you should ask yourself.
Have you already tried traditional data analytics/statistics?
Machine learning is only suitable in environments where the workload is complex and human labor is unable to fulfill the company’s requisitions. Try traditional approach, and if it cannot solve your problem then only you should approach machine learning system.
For instance, if you run a data center, you can use machine learning to reduce energy consumption – perhaps, by defining correct means to optimize electricity consumption, water pumps, room temperature, and IT load. However, integrating machine learning is a hectic process as it requires a lot of time and money. Hence machine learning is only intended for an organization with complex work operations. So, before seeking aid from Machine Learning, step back and think whether or not traditional data analytics solves your problem.
How machine learning solves your problems?
If you want to understand how the machineries inside your organizations are performing, you need machine learning system. It assists in predictive maintenance and estimates the life of particular machinery. To get real-time data, you’ll need sensors to collect information such as:
- The total cycle of the machine.
- How old it is.
- Vibration it’s experiencing
Simply put, machine learning algorithm is useless without relevant data.
Do you have adequate relevant data?
To train your machine learning model, you’ll need a large amount of previous company data. For them to work accurately, you’ll hundreds and thousands of data points. It’ll help you pre-train your model and ensures smooth flow of work.
Before initiating your steps towards complex machine learning analytics, start simple and slow with traditional statistics. From there, you can start to integrate machine learning into your system. Also, consult experts familiar with a variety of models available in the market, they can help you select the right solution for your business.