Prerequisite: None
In today's data-driven landscape, the success of machine learning (ML) and artificial intelligence (AI) initiatives hinges on robust data management strategies.
This session will focus on key aspects of data management strategies for ML/AI, addressing critical topics such as data quality, preparation, and integration. This session will review harnessing the power of data lakes, data warehouses, and data catalogs to fuel your ML/AI initiatives.
Topics to include:
- Discover techniques to assess and elevate data quality, ensuring trustworthiness and reliability for your ML and AI initiatives
- Optimize your data sets for high-performing ML models through effective data preprocessing and transformation
- Understand how to strategically use data lakes, data warehouses, and data catalogs to support your ML and AI objectives
- Learn to design and manage efficient data pipelines that ensure a smooth data flow from source to model
- Master the art of feature engineering to enhance the predictive power of your ML models