Dates and location
Pricing
Hours
Dates and location
Pricing
Hours
Description
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 course, the instructor 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. Understand why the traditional data quality methods developed for tabular data used in BI applications are no longer sufficient.
Using real-world examples, understand how data augmentation and generation of synthetic data can improve the performance of AI models by making them more generalizable. Examine the data-centric movement, including open source and commercial data quality improvement tools. Review how 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.
Topics Include:
- Data Quality for BI vs. AI
- State of AI
- When AI Gets It Wrong
- AI System Engineering
- Data Quality
- Case Studies
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Key Takeaways
Upon completion of this course, learners will understand:
- 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.
Who Will Benefit
This course will benefit professionals who work with, oversee or rely on AI-driven tools and data informed decision-making by equipping them with a practical understanding of how data quality impacts AI performance and reliability.
How to Access the Course
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To access the course on your computer please visit our BlackBoard site, and log-in using the same login and password used for the Registration Portal.
Please allow up to 15 minutes after registration for the course to appear on your BlackBoard page.
Registration, cancellation, withdrawal and all other CPA Ontario PD policies can be found here.
Speaker(s)
TDWI is the leading provider of education and research for business intelligence, analytics, and data management professionals. TDWI’s vendor-neutral training is led by experienced practitioners, and has earned a world-wide reputation for being comprehensive, practical, and actionable.
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).