Author: Zen and the Art of Computer Programming
1 Introduction
Data-driven AI development has become an AI application model in the new era. It can bring new business value, but it also faces a series of challenges such as data volume, data quality, difficulty in data collection, and difficulty in modeling. In this article, we will systematically explain the theoretical foundation, methodology and practical skills of data-driven AI development.
Data-driven AI development includes the following three steps: data collection, data cleaning, data annotation and data enhancement, and finally forms a data set of a certain scale. Then use this data set to train and verify the model, and then obtain a model with strong predictive ability and good effect. In addition, since data-driven AI development needs to process multiple modalities of data (such as text, images, audio), data preprocessing, feature engineering, sample generation, model fusion and other technologies must be used together to achieve efficient and accurate predictions.
The article also provides some solutions to the challenges faced by data-driven AI development. For example, how to design appropriate evaluation indicators and improve the quality of data sets by analyzing the correlation and connections between data; how to introduce unlabeled data using semi-supervised learning or weakly supervised learning to improve the generalization ability of the model ; How to use multi-modal data sets to train different types of models, and combine the prediction results of the models to discover hidden information in the business; how to use artificial intelligence technology to assist operations, identify and interpret customer needs, and improve the interactive experience of products.
During the writing process, the author will explain data-driven AI development from different perspectives, striving to give readers a deeper understanding. I hope that this article can help readers clarify the knowledge structure, improve self-understanding and ability competitiveness, and promote the development of data-driven AI development.
2. Explanation of basic concepts and terms
2.1 Modality
Modality refers to the form, expression, or expressiveness of data. There are various modalities in the real world, such as text, pictures, videos, sounds, etc.