Properly implemented Machine Learning (ML) models can have a positive effect on organizational efficiency. It is first necessary to understand how these models are created, how they function, and how they are put into production.

The Definition of a Machine Learning Model

When a computer is presented with questions within a particular domain, a machine learning model will run an algorithm that will enable it to resolve those questions. These algorithms are not necessarily limited to particular scenarios, but can be programmed to a higher degree of accuracy for certain types of questions. Use cases for these are listed below.

• Regression questions, such as ‘How much’ and ‘how many’. For example, how much will my car be worth in two years?
• Classification questions, such as ‘Type of object’. For example, what to class does this object belong?
• Clustering or grouping questions. For example, what are the different clusters for this particular set of objects?
• Abnormality detection questions. For example, is this object abnormal based on what is defined as normal?

Using tools, frameworks and codes, these models are built by engineers and data scientists based on what is often a huge amount of data.

To build a really effective machine learning model, massive amounts of data are needed. This data needs to be cleaned and labelled. It is an iterative process, involving trial and error, as well as tests and measures. Fundamentally, there are many steps and processes involved in creating a functional model. Once this model is created, the computer will be able to answer questions for different cases within a particular scenario.

The machine learning model is used to find answers to specific questions regarding different cases. Each model is specific to a particular scenario. For example, is an issue with a product fixable or not, or is this set of symptoms indicative of a particular medical problem, or is this a legitimate bank transaction? In other words, a computer can suggest a solution with a certain degree of accuracy based on the data that is used to create the machine learning model.

How Can Machine Learning Models Help Us?

The goal of every Machine Learning model is to achieve the following:

• Integrate workflows and processes that involve multiple participants
• Enable information systems to utilize certain algorithms with minimal code revision
• Provide analytics as a service by sharing the model between multiple use cases
• Use real batch or on-the-fly cases to integrate the model systematically
• Combine multiple models to answer complex questions requiring multi-step answers
• Use models in decision making within the organization or with external customers.

The ability to monitor and measure the behavior of the models in a live environment is critical. This facilitates a cycle of constant improvement. Individualized models are generally not as useful as those that are part of a more sophisticated deployment involving multiple scenarios. In such cases the solutions suggested by the model need to go to a decision model based on a domain expert’s knowledge and implemented by certain business rules.

Let’s take the example of car insurance. A machine learning model will be designed by an insurance company using their own data sets that detail stolen cars. The model will categorize a car as low, medium or high risk.

As such, calculating an insurance quote for a particular car would involve calling to a Machine Learning model which will then identify the likelihood of it being stolen and send the result to another part of the quotation process to calculate a cost for an insurance policy. In this case, the Machine Learning model is integrated into the Quote Generation Process.

Conclusion

Machine Learning models are most useful when they are integrated as part of a business decision to deliver business value. It is crucial that these models are able to execute requests on-the-fly. The performance of these models in a specific context must be monitored, measured, and improved over time.

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