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Common Issues Related to Machine Learning Models
Fremont, CA: Organizations are constantly battling to put their machine learning models into production and use them to improve their operations. Data scientists created machine learning models, but they are usually uninformed of the production elements of deploying or scoring such models. If something goes wrong, they typically avoid handling the production. It's also not usually their role to perform DevOps chores like model deployment. These DevOps activities and the job of data scientists have always got separated.
Let see some of the issues that machine learning models face in production while all of this is going on in the background.
- Periodic Redeployment of Machine Learning Models
Because machine learning models degrade with time, they must get deployed repeatedly. It's in direct opposition to software engineering concepts used by programmers. In their case, the code deployed once is good for life, and it must only get redeployed when it gets upgraded. Machine learning models, on the other hand, may lose their value with time. It must be taken care of during the model's lifespan and requires constant monitoring.
- All About the Monitoring
In contrast to software engineering code, machine learning models may take more work to monitor. Because these models get trained on data before being deployed, the data must be accurate and free of any ambiguous abnormalities. In most cases, tracking is necessary for incoming feature vectors to identify drift, bias, or abnormalities in data. Keeping this in mind, it's also critical to have incoming data monitoring and notifications.
Machine learning models must also comply with regulatory requirements before being implemented in production. It's especially true in the banking and finance industries, where model predictions must get monitored promptly and ready to verify compliance to authorities and explain why the machine learning model predicted a specific price. Different forecasts may exist, and historical data must get examined to ensure that the machine learning model operates effectively. Tracking is necessary to discover the model quickly and the dataset it was trained on to find a prediction made by a previous model.