If you're looking to build a Multi-Lingual Query Engine that combines natural language to SQL generation with query execution while fully leveraging MLflow’s features, this blog post is your guide. We’ll explore how to leverage MLflow Models from Code to enable seamless tracking and versioning of AI Workflows. Additionally, we’ll deep dive into MLflow’s Tracing feature, which introduces observability into the many different components of an AI Workflow by tracking inputs, outputs, and metadata at every intermediate step.
From Natural Language to SQL: Building and Tracking a Multi-Lingual Query Engine
· 40 min read
Machine Learning Analyst at adidas
Machine Learning Engineer at adidas
Sr. Solutions Architect at adidas