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There are several steps that can be taken to transition from preparing data to optimizing models and tuning hyperparameters:

1. Define the problem and the performance metric: It is important to start by clearly defining the problem and the performance metric that will be used to evaluate the model's performance. This will help guide the optimization process and ensure that the model is optimized for the desired outcome.

2. Establish baselines: It is important to establish a baseline performance for the model before optimizing. This will give a clear understanding of the performance improvements obtained after optimization.

3. Select appropriate algorithms: Once the problem and performance metric are defined, it is necessary to select appropriate algorithms. Different algorithms will perform differently on different datasets and problems. Therefore, it is important to experiment with different algorithms to determine the best fit.

4. Prepare data: It is important to prepare data for use in the model. This involves cleaning, preprocessing, and transforming data into formats that can be used by the algorithm.

5. Optimize hyperparameters: Once the model is defined and the data is prepared, it is time to optimize hyperparameters. This involves tuning the model's parameters to achieve the best possible performance.

6. Evaluate performance: After optimizing the model, it is important to evaluate performance on both the training and test data. This will help to determine if the model is overfitting or underfitting.

7. Repeat: The optimization process is iterative, and it may be necessary to repeat the process several times until the desired levels of performance are achieved.