A Collaborative Data Science Development Workflow Using Kedro and MLflow

Explore a 24-minute video presentation on developing an efficient and scalable collaborative data science workflow. Learn about a solution that incorporates Kedro pipelines, MLflow tracking, and cloud-agnostic GPU-enabled containers. Discover how data scientists can individually build and test pipelines, measure performance, and transition strong solutions to production. Gain insights into the architecture and core components, including Docker, Kedrow, data engineering conventions, MLflow logging, Databricks, and Spark. Understand the process of serving production-worthy models to applications through MLflow in this comprehensive overview of a modern data science development workflow.

Introduction
Overview
Agenda
Objectives
Core Components
Docker
Kedrow
Data Engineering Convention
ML Flow
ML Flow Logging
Databricks
Spark
Architecture