Machine learning models used for predictive maintenance rely on quality data for training. Unclean data can negatively impact the learning process, resulting in poorly performing models, but also undermine user trust, which can sabotage a whole product track. It is known, that garbage in, garbage out is the core of the problem. But there is a bit more to it, especially in large organisations.
Join this session to gain insights into this problem, including:
- Why to implement data validation check to ensure is accurate and complete before processing?
- How to develop scripts to automate repetitive cleaning tasks and reducing manual errors
- Why to conduct regular data audits for identification and correction?
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