Designing a learning system in machine learning is a complex task that involves several key steps. Here’s an outline of the process that can help you design an effective learning system:

1. Define the Problem: Determine what problem you’re trying to solve with machine learning. It could be a classification, regression, clustering, or any other specific task.
2. Collect and Preprocess the Data: Acquire the data related to the problem, clean it, preprocess it, and divide it into training and testing sets.
3. Choose the Model: Depending on the problem, select an appropriate model such as SVM, Neural Networks, Random Forest, etc.
4. Feature Engineering: Extract and select the most relevant features that will help in training the model.
5. Training the Model: Utilize the training set to train the model by adjusting its parameters. This might involve techniques like gradient descent, optimization algorithms, etc.
6. Validation and Tuning: Apply validation techniques like cross-validation to tune hyperparameters and select the best model.
7. Evaluation: Evaluate the model on the test set using appropriate metrics like accuracy, precision, recall, F1-score, etc.
8. Deployment: If the model is performing satisfactorily, deploy it into a production environment.
9. Monitoring and Maintenance: Continuously monitor the model’s performance and make necessary updates as the data evolves.
10. Ethical Considerations: Ensure that the model complies with all relevant ethical guidelines and regulations.
11. Optimization for Scaling: If you are sending the model’s predictions as part of a bulk operation, make sure that the system is optimized for handling large-scale data to ensure efficiency and that it meets any specific requirements for not being classified as spam.
12. Documentation: Keep thorough documentation of all the steps, methodologies, and decisions made throughout the design process.

Remember, building a learning system is an iterative process, and continuous refinement and tuning may be necessary to achieve optimal results. Collaborating with domain experts, data scientists, engineers, and other stakeholders is often essential for a successful project.