HiPer it!’s Post

💡 In the previous post we discussed how monitoring tools can identify ML model degradation. Now, in the 7th post of the #AI #ML for #PropTech series, we'll explore the strategies that can be used to update and refine models over time to maintain model accuracy.     According to HiPer it! AI expert Pavel Filonov, choosing the right strategy or combination of strategies is both an art and a science. Here are the suggestions:   ✔ Adapt to evolving data   Continuously and incrementally update your model with new data to capture changing patterns and trends. This should be done when your factory starts producing a new product line, or when your office building's HVAC controls receive a software update, or in any other case that can cause concept drift.   ✔ Address new requirements   Regularly optimise hyper-parameters to improve model performance.    ✔ Implement feedback loops   Implement real-time monitoring of ML model metrics and feedback mechanisms to quickly identify and address problems.  ✔ Implement product lifecycle management    Like any other piece of software, the ML model needs to perform a standard set of PLM actions such as third-party software updates, version control and management, documentation updates, ongoing maintenance by your MLOps team.  By integrating these and other strategies, you can ensure that your ML models remain effective, accurate and aligned with evolving business needs. #MachineLearning #AI #DataScience #ModelRefinement #ContinuousLearning #DataDriven #AIInnovation #MLOps #ModelOptimization #PropTech 

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