MeetupTecton 0.6: Notebook-driven DevelopmentClaimNotebook-driven development can help data engineering, data science, and ML engineering teams improve feature engineering workflows and shorten iteration loops.4:34
Feature stores, and second thoughts in 2023
7 sessions
MeetupTecton 0.6: Notebook-driven Development
PodcastMLOps Build or Buy, Startup vs. Enterprise?ClaimSlack can often add a new recommender quickly by adjusting the features, feature weights, and candidate-fetching method for a use case.16:10
Using Vector Databases: Practical Advice for ProductionClaimCombining a feature store with a vector database lets an application inject current user or product information into a prompt.12:15
LLM-based Feature Extraction for Operational OptimizationClaimXin Lian says LLMs can be used as a middle layer between upstream and downstream tasks to extract features for operational optimization.2:53
PodcastTecton Round-table // Get your ML Application Into ProductionClaimEddie Esquivel says the biggest production challenge is organizational separation, where data scientists develop models and features before handing them to an often understaffed ML or engineering team.3:36
PodcastThe Future of Feature Stores and PlatformsPushed backMike Del Balso said feature templates and reusable feature sets are appealing, but customers' differing data and requirements make simple copy-and-paste solutions unlikely to solve most problems.1:06:26
Product Strategy for LLM Features When LLMs Aren't Your ProductPushed backLLM-based feature engineering should not be viewed only as a prototyping or brainstorming technique; it can be suitable for production in some cases.11:30