Level: Beginner to Intermediate
Prerequisite: None
Norbert Kremer, Ph.D.
CBIP, AICP
Cloud Solution Architect
Analytics By Design
Norbert Kremer, Ph.D., is a recognized expert on cloud data platforms, including their scalability, performance, and cost characteristics. He has worked as a data engineer on large projects as well as a solution architect covering a wide range of cloud services. He is a Google Authorized Trainer and holds multiple Google Cloud Professional certifications. Other areas of interest include multicloud architectures, generative AI involving integration of LLMs with data in corporate data stores, and cloud cost optimization (FinOps).
This is part of an optional week-long series.
Take all the courses to earn a certificate, or attend this individual course.
Take control of your AI model outcomes by mastering data quality, a critical factor in both training and inference phases. This course offers a deep dive into data quality assessment and improvement practices that drive more reliable, more accurate, and more cost-effective AI solutions.
In this masterclass, Norbert Kremer will examine how data is used across a variety of AI use cases. He will show how AI models with different modalities and scales use training data in very different ways and explain why the large scale of some current models demands new methods of generating training data. Kremer will show that traditional data quality methods developed for tabular data used in BI applications are no longer sufficient. He will show how the economics of building training data sets leads to major innovations in the field.
Using real-world examples, Kremer will demonstrate how data augmentation and generation of synthetic data can improve the performance of AI models by making them more generalizable. He will examine the data-centric movement, including open source and commercial data quality improvement tools. Kremer will show that assessing data quality and improving it as part of iterative AI model development can significantly enhance AI model performance while also managing cost and complexity.
You Will Learn
- How data quality for AI differs from data quality for BI
- Theory of how data is used to train different types of AI models
- Data quality practices and improvement techniques for different types of AI models
- Data-centric AI: stop tinkering with code and focus on data engineering
- How economics drives decisions to collect more data, improve labeling, or improve model code
- The importance of working iteratively to improve data quality, measure performance, and monitor for data drift
Geared To
- Data scientists and AI developers
- Data engineers
- AI engineers
- Business leaders deploying AI models
- FinOps practitioners concerned with AI training costs and AI serving costs and associated unit economics