You are tasked with developing a machine learning model to forecast daily temperatures for a weather forecasting application. The dataset includes hourly temperature readings over several years. Initially, you randomly split the data into training and test sets, applied necessary transformations, and achieved a testing accuracy of 97%. However, upon deployment, the model's accuracy significantly dropped to 66%. Considering the time-sensitive nature of the data and the need for high accuracy in production, which strategies would you implement to improve the model's production accuracy? (Choose two options) | Google Professional Machine Learning Engineer Quiz