Table Overview: Challenges of Machine Learning in Predictive Autoscaling

Table Overview: Challenges of Machine Learning in Predictive Autoscaling.md

Aspect	Description
Continuous Improvement	Need for continuous updates and refinements to maintain accuracy and effectiveness of machine learning models in a rapidly changing environment
Cost	Training the model can be quite costly
Data Quality	Ensuring high-quality, relevant data for training accurate machine learning models, especially when dealing with diverse data sources
Ethical Considerations	Concerns around data privacy, security, and potential biases in decision-making processes when using machine learning
Explainability and Interpretability	Difficulty in understanding how machine learning models arrive at their predictions, making it challenging to identify and address biases or incorrect assumptions
Human Element	Importance of human expertise to validate machine learning models, interpret results, and make strategic decisions; ensuring effective collaboration between humans and machines is crucial
Model Complexity	Developing simple yet effective machine learning models that can account for various factors influencing demand and resource usage patterns
Overfitting and Underfitting	Risk of overfitting models to historical data, resulting in poor generalization performance for new inputs; similarly, underfitting occurs when models are too simple and fail to capture important patterns in the data
Training Time	Training machine learning models requires significant computational resources and time, especially for large datasets