In 2013, IBM and the University of Texas Anderson Cancer Center developed an AI-based Oncology Expert Advisor that analyzes patients' medical records to provide assistive solutions for oncologists. The project cost over $62 million and demonstrated poor recommendations. In 2016, Microsoft's "Tay" chatbot was designed to have playful conversations with users but was corrupted by Twitter users within 24 hours. Amazon attempted to create an AI-powered resume screening system that penalized resumes including terms like "woman," highlighting the industry's bias against female candidates. Advanced machine learning models are often black box algorithms, making it challenging to interpret their decision-making process. The question arises as to how to monitor a model's performance once trained. Tracking a model's performance is difficult due to production data distribution differences from training or validation data. Various methods can be used to evaluate and improve models, including offline methods such as examining distributions of features or testing assumptions (correlations). These methods help detect potential issues like model drift, which occurs when the model's performance degrades over time due to changes in the data distribution. Retraining a model on new data can solve this issue, but it requires careful consideration of factors such as data quantity, frequency, and deployment strategy. The use of containers, Kubernetes, and online learning methods can facilitate model retraining and deployment. Ultimately, effective model evaluation and maintenance are crucial to ensure the success of machine learning projects in various industries.