Machine Learning: Business Products and Services{0}

By Alex C.

Business products and services is something that companies purchase for their own productions or operations. This includes many things such as component parts, raw materials, and any service that assists in the operation of a firm. It is hard to distinguish between a consumer and business product, but they are set apart by the end user. If a product is to be used for personal use, then it is a consumer product while a product used for a company or to be made into a product is a business product. Machine learning is artificial intelligence where computers can learn without being explicitly programmed. Most machine learning methods include supervised learning, where algorithms use labeled examples, and unsupervised learning, where data has no historical labels. When machine learning is applied to business products and services, it deals with the behind the scenes to help a business operate.

Some benefits of machine learning in business products and services is the ability to analyze large amounts of data accurately without human intervention. For example, a publishing platform like Disqus can find spam within comments without human intervention. A very important benefit of machine learning, however, is the ability to work with big data. Collecting, storing, and moving big data could be a huge hassle, but machine learning can deal with it easily. As a result, the amount of time spent with these tasks is reduced along with the costs.

There are many tools ready for companies to use to improve their system. One popular tool called SumoLogic is used by companies such as Adobe, Kaiser Permanente, and McGrawHill to support business apps as well as the IT infrastructure. It works by collecting all the data and centralizing it into logs. Then all the logs are analyzed and monitored to visualize abnormalities which are detected and as a result, notifies the user. This allows companies to spend less time in finding issues in the IT department since the issues is more easily solved. Another tool called Skytree creates models with machine learning. Those models are then compared with past models to find the most accurate rendition to put into production. So instead of having to create as many models as possible to compare to, machine learning can do it for a company.

However, there are limitations to machine learning. The data that is being fed must be free of anomalies and, in short, clean. This data must also be restructured to be suited for machine learning. A data scientist is still needed in this automation system to set up the data before the machine learning can do anything. Another limitation of machine learning is the Pareto principle. This principle states that 20% of the input is responsible for 80% of the output which somewhat applies to the previous limitation. If companies use the same products for the same needs, then their needs would probably be in the 80% which is already made, but if anything new or creative is wanted, then a data scientist would be needed to work in the 80% of the input.

Machine learning can seem to do almost anything for business products and services when it comes to assisting in operations. Everything such as IT infrastructure, big data, content, and opinions can be moderated with ease which brings in benefits of less time in certain activities and less money spent in those areas. However, someone must still be behind the scenes to set everything up or to improve the system to work with machine learning. Nevertheless, machine learning has still made a huge impact and changed or optimized how businesses can operate behind the scenes.


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