Bayesian Models | Insights into components of prior research and aspects of latent academia
Bayesian Models | Insights into components of prior research and aspects of latent academia
Description: Elasticsearch and Artificial Intelligence
Elasticsearch and Artificial Intelligence
For this week in machine learning, I am sharing two interesting tutorials from VLDB and KDD conferences this week.
Machine learning pipeline is an iterative process involving data curation, feature engineering, training and deploying models, as well as monitoring and maintenance of the deployed models. In large system with many downstream tasks, a feature store is important to standardize and manage feature generation and workflows in using the features. With the advent of self-supervised pre-trained embedding models as features, feature store faces new challenges to manage embeddings.
Bayesian Models | Insights into components of prior research and aspects of latent academia
Bayesian Models | Insights into components of prior research and aspects of latent academia