A machine learning model is one of the best kind of file that has been created by machine and trained to identify some types of patterns. In this case, you need to train a model over some set of data while offering it an algorithm, which it may use to work properly while learning from those sets of data.
With the help of ML model monitoring, you can make predictions about some specific data. However, in some cases, ML model monitoring can fail.
As per some research, just 53% of models of machine learning can convert into production from the prototype. So, you can also say that almost 47% of those models turn into failures.
Hence, if an organization is trying to implement a solution regarding machine learning, there can be a possibility that it can turn into a failure. So, now you may wonder what the reasons for the failure of ML model monitoring are. So, discussed here are the reasons why the ML model fails.
Reasons why machine learning models Will fail
There are multiple factors that can be the reason for the failure or success of your project regarding machine learning model monitoring. Those reasons may be:
A crucial reason for the failure of ML model monitoring can be data. Machine models rely greatly on data. For instance, if you train a specific model to identify apples from an image, and the data you have is the photo of oranges, then your model will not know what the look of an apple is.
Hence, that kind of model cannot offer proper results. There are several factors that may have a good impact on the procedure regarding model training. For example:
- The quality of data
- Data availability is crucial for the problem
- Data bias
- Relevant the main issue
Though you can get the data that is most relevant to the issue, still, a lot of your time needs to be spent on cleansing tasks and data quality. The data is important for training of machine learning model has sharp qualities.
If someone is passing the data of low quality during training of machine learning model, it will result in badly models, which may lead to faulty predictions.
In addition to this, even if the process of training is planned to change data quality, the model may encounter bad data when the prediction process is taking place. The reason is the data you have for training the model is a representation of the data that will be encountered by the model when it is deployed in production.
The solution to this issue
It is crucial to incorporate the standards regarding data quality in the project and properly communicate with the customer to recognize and eliminate the main reason for the bad data issue.
The stakeholders and business owners have high expectations for machine learning models while starting a project. However, there can be issues if the machine learning model and the expectations of the business owners cannot align with the ultimate goal.
If there is no specific goal and a specific problem that the scientists need to solve, then no amount of modeling work or no amount of the analysis of data can make the project successful.
So, to remove this issue, a proper understanding of the data maturity of the organization can be helpful for recognizing if the issue actually needs an analytics solution or a machine learning solution.
Solution of the issue
So, before starting the project, it is vital to listen to the issues of the customers. This can help to get multiple solutions regarding the machine learning solution. Therefore, there are a few reasons for the failure of an ML model monitoring; however, those issues have their own solutions as well .Masters In AI and Machine Lerning In Sydney