Join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest Nicolas Omont from Dataiku, a leader across the entire AI lifecycle.
In this 1-hour webinar, you will discover:
- The types of ML models where the human element is most critical
- How non-empirical factors figure into the model fairness equation
- The respective roles of data scientists and ML engineers in the ML monitoring process
- Automated and human components of model explainability
Machine learning (ML) and ML operations platforms are becoming increasingly popular and sophisticated. That’s a good thing, as it transforms AI initiatives from science projects to rigorous engineering efforts. But with such platforms comes the temptation of automation, scripting the whole ML process, not just optimizing models, but monitoring their drift in accuracy and retraining them. While some automation is good, humans play a critical role.
Elements of fairness are contextual and involve tradeoffs. Changes in data may require retraining or restructuring a model’s features, depending on circumstances and current events. All of this requires human judgment, carefully integrated with automated management and algorithmic learning. Humans have to be part of the workflow, included in the feedback loop, and involved in the process.