Data Latency is the ability to load and update data in near-real time, while supporting query workloads at the same time.
Self-supervised learning is an approach that aims to solve this problem. This method allows models to learn from unlabeled data and greatly reduces the need for data labeling.
Pre-training refers to pre-training a model on large data sets and then fine-tuning it to perform a specific task. This technique is widely used, especially in areas such as natural language processing (NLP) and image processing.
Foundation Models (FMs) refer to structures trained on large data sets in the field of artificial intelligence and machine learning, versatile and usable in a variety of applications.
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