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Common Problems You May Face with Machine Learning Models
Machine learning models must also pass regulatory compliance before being implemented in production.
FREMONT, CA: Organizations are constantly battling to put their machine learning models into production and use them to improve their operations. Data scientists create machine learning models, but they are often uninformed of the production aspects of deploying or scoring those models. As a result, they usually avoid interfering with manufacturing in case something goes wrong. It is also not usually their role to perform DevOps duties such as model deployment. As a result, these DevOps functions and the work of data scientists have traditionally getting separated.
Let's see some of the issues that machine learning models face in production.
Periodic Redeployment of Machine Learning Models
Because machine learning models degrade with time, they must get deployed again and again. It's in direct opposition to the software engineering principles followed by software developers. In their case, code that gets deployed once is fine for all time, and only when the code gets upgraded does it need redeploying. However, machine learning models may lose their usefulness with time. Therefore, it must be addressed over the model's lifespan and requires regular monitoring.
All About the Monitoring
In contrast to software engineering code, monitoring machine learning models may necessitate additional effort. Because these models are trained on data and subsequently deployed, the data must be precise and free of any uncertain abnormalities. Thus, most of the time, establishing tracking for incoming feature vectors to detect drift, bias, or anomalies in data. Keeping this in mind, it is also critical to have data monitoring and notifications in place.
Machine learning models must also pass regulatory compliance before being implemented in production. Different predictions may be made and evaluating history to ensure that the machine learning model is acting correctly. It's especially true in the banking or finance industry, where model predictions get tracked promptly and quickly to demonstrate compliance to authorities and explain why the machine learning model predicted a specific price. Tracking must get built in to discover the model promptly and the dataset it was trained on to find a prophecy by a model in the past.