In this use case, you will take on the persona of a machine learning engineer for a premium music service. After spending a great deal of time collecting customer data, training, and testing various models, your team has built an ML-powered recommendation engine to give your listeners personalized playlist recommendations based on their most listened to songs on their sound cloud. After you push your model into production, something goes wrong. You realize that your production model has no tools available to monitor your model performance, identify root cause issues, or gain insights into how to actively improve your model when things inevitably go wrong. Now, you need to learn how to use an ML observability tool.