Explore a data-centric approach to Machine Learning development in this 30-minute talk by Jimmy Whitaker, an expert in ML and NLP. Discover the latest advancements in AI, understand the concept of Data-Centric AI, and learn how to set up for effective iteration. Gain insights on using great tools to create outstanding products, simplifying analytics processes, and addressing data quality as a team-wide responsibility. The presentation covers key topics such as the current state of AI, the shift towards Data-Centric AI, and practical strategies for solving problems one at a time while ensuring reproducibility in analytics.
Rethinking ML Development - A Data-Centric Approach
Overview
Syllabus
- Introductions
- Agenda
- AI Today
- What is Data-Centric AI?
- The Move to Data-Centric AI
- Set Yourself up for Iteration
- Great Tools Help Make Great Products
- Make it easy to write, test, run, and reproduce your analytics
- Solve one problem at a time
- Quality is everyone’s problem, especially with data
- Recap
Taught by
Open Data Science
Related Courses
-
GenAI in Data Analytics
-
Data-Centric AI Approach in Pharmaceutical Industry - AstraZeneca Case Study
-
How Top Healthcare Companies Boost AI Development with Data-Centric Approaches
-
Want High Performing LLMs? It's All About Your Data - Importance of Quality Data in AI Development
-
Solving the Last Mile Problem of Foundation Models with Data-Centric AI
-
Data-Centric AI in Comcast's Voice and Conversational Interfaces
