Tackling Data Drift

Addressing the challenges of data quality is crucial before tackling issues like concept drift. The need for better visual tools to guide customers in data labeling is emphasized, alongside the importance of active learning in model training. A promising project on task mining aims to automate the identification of processes by analyzing user interactions, showcasing a significant opportunity for innovation in the field.

In this clip
From this podcast
Gradient Dissent - A Machine Learning Podcast
Mircea Neagovici — Robotic Process Automation (RPA) and ML
Related Questions
- What are the challenges in machine learning as discussed in the episode MLOps Meetup #23 // Monitoring the ML stack // Lina Weichbrodt and the clip Monitoring Machine Learning?
- Is less labeled data needed for training machine learning models according to the episode The Fallacy of "Ground Truth" with Shayan Mohanty - #576 and the clip Active Learning Insights?

Tackling Data Drift

In this clip

From this podcast

Gradient Dissent - A Machine Learning Podcast

Mircea Neagovici — Robotic Process Automation (RPA) and ML

Related Questions

What are the challenges in machine learning as discussed in the episode MLOps Meetup #23 // Monitoring the ML stack // Lina Weichbrodt and the clip Monitoring Machine Learning?

Is less labeled data needed for training machine learning models according to the episode The Fallacy of "Ground Truth" with Shayan Mohanty - #576 and the clip Active Learning Insights?