Can we use streaming data to train and use a model continuously and improve it at the same time?
The ability to use streaming data for both continuous model training and real-time inference is a significant topic in machine learning, particularly within modern data-driven applications. The traditional approach to building machine learning models typically involves collecting a batch of data, cleaning and preparing it, training a model, evaluating it, deploying it, and then periodically
Why is machine learning still weak with streamed data (for example, trading)? Is it because of data (not enough diversity to get the patterns) or too much noise?
Machine learning's comparatively limited effectiveness with streamed data, particularly in high-frequency and financial trading contexts, derives from a combination of inherent data characteristics and structural limitations of current machine learning paradigms. Two central challenges are the nature of the data itself—specifically its high noise content and non-stationarity—and the technical demands of real-time adaptation and generalization
Is it necessary to use other data for training and evaluation of the model?
In the field of machine learning, the use of additional data for training and evaluation of models is indeed necessary. While it is possible to train and evaluate models using a single dataset, the inclusion of other data can greatly enhance the performance and generalization capabilities of the model. This is especially true in the
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Is it possible to reuse training sets iteratively and what impact does that have on the performance of the trained model?
Iteratively reusing training sets in machine learning is a common practice that can have a significant impact on the performance of the trained model. By repeatedly using the same training data, the model can learn from its mistakes and improve its predictive capabilities. However, it is essential to understand the potential advantages and disadvantages of
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning