Explore the world of Machine Learning Operations (MLOps) in this insightful podcast episode featuring Niklas Kühl, an expert in applied AI and data science. Delve into the challenges of automating and operationalizing ML products, and understand why MLOps is crucial for successful industrial machine learning projects. Learn about Niklas' background in industrial engineering and his current roles in academia and industry. Discover key topics such as Twitter data scraping, NLP usage, consumer behavior prediction, and communication between management and ML systems. Gain valuable insights on budgeting ML products, defining MLOps, navigating its implementation, and measuring return on investment. Explore the concept of full-stack data scientists and how to define success in various data science projects. Engage with discussions on foundational models and get a glimpse into Niklas' day-to-day life in this comprehensive exploration of MLOps and its significance in the evolving landscape of machine learning.
Machine Learning Operations - What is it and Why Do We Need It?
Overview
Syllabus
[] Niklas' preferred coffee
[] Introduction to Niklas Kühl
[] Takeaways
[] Subscribe to our newsletters and give us a rating here!
[] Niklas background
[] Scraping Twitter data
[] EV's conclusions
[] NLP usage on Twitter
[] Consumer behavior production
[] Management and Machine Learning Systems Communication
[] Current hype around Machine Learning
[] Budgeting ML Products
[] Machine Learning Operations MLOps: Overview, Definition, and Architecture paper
[] Niklas' MLOps definiton
[] Navigating the idea of MLOps
[] Return of Investment endeavor
[] Full stack data scientist
[] Defining success for different kinds of data science projects
[] Fun fact about Niklas
[] Other things Niklas do
[] The world is your oyster
[] Niklas' day-to-day life
[] One lecture Niklas can drop in
[] Foundational models
[] Wrap up
Taught by
MLOps.community
