Podcast: Play in new window | Embed
Subscribe: Apple Podcasts | Google Podcasts | Spotify | Amazon Music | Email | TuneIn | Deezer | RSS
Data is the heart of AI. So, therefore doing things associated with your data is going to be critical for AI projects. This includes Data Preparation, Data Cleaning, Data Splitting, Data Multiplication, and Data Transformation.
In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms above and explain why they are important for AI projects. Data Preparation is all the necessary activities that need to be performed on data to get the data ready to be used for an analytics or machine learning project. Data preparation includes aspects of data cleaning, data transformation, data wrangling, data augmentation, anonymization, and other actions to be performed on data. These are usually performed as part of a data engineering data pipeline. In this episode we explain how these terms relate to AI and why it’s important to know about them.
Show Notes:
- FREE Intro to CPMAI mini course
- CPMAI Training and Certification
- What is the Certified Professional in AI Project Management (CPMAI) Certification?
- The Steps for a Machine Learning Project
- AI Glossary
- AI Glossary Series – DevOps, Machine Learning Operations (ML Ops)
- AI Glossary Series – Automated Machine Learning (AutoML)
