Writing Clean, Production-Level ML Code with Laszlo Sragner

Introduction

Data Science, Code Quality Hierarchy of Needs

What Is the Hierarchy of Needs?

The Most Important Concept in Clean Code and Code Quality

Data Scientists - What Is the Definition of Technical Debt?

Machine Learning - Technical Debt and Technical Mess

Code Refactoring

Code Reviews for ML Projects?

Using Layers of Abstraction in Python

How to Release New Better Machine Learning Models Without Much Downtime?

Refactoring and Clean Code Enables DevOps

Data Scientists - Is Blue Code a Red Flag?

Developing a Testable Architecture for a ML Project

What Are Your Best Resources for Data Scientists to Learn How to Write Clean Code?

How to Build a Microservice?

Data Drift Monitoring

How Do You Structure ML Projects to Reduce Technical Debts?

MLOps Live - Part 2