No, you don't always need vast amounts of data to start with machine learning. While having more data can improve the accuracy of models, especially for complex problems, there are several techniques and approaches suited for smaller datasets. Some algorithms, like decision trees or linear regression, can work well with limited data. Additionally, techniques like data augmentation, transfer learning, and leveraging pre-trained models can help when you have limited initial data. It's essential to focus on data quality and relevance, as having meaningful data can sometimes be more beneficial than just having vast quantities.