We noticed that the industry is laser-focused on tackling feature drift after production, but we spotted a gap. Most ML teams are wrestling with model validation even before anything is deployed in production. We also noticed that post-deployment analysis sometimes misses the mark, lacking components like identifying underperforming cohorts or giving actionable insights. This leads to a barrage of alerts and the inevitable alert fatigue.
We decided to start Openlayer to offer a more holistic solution that helps teams from the ML development and experiment tracking phase to more advanced tasks like monitoring and fairness. We established a strong baseline with this launch and are now building several features on top.
We noticed that the industry is laser-focused on tackling feature drift after production, but we spotted a gap. Most ML teams are wrestling with model validation even before anything is deployed in production. We also noticed that post-deployment analysis sometimes misses the mark, lacking components like identifying underperforming cohorts or giving actionable insights. This leads to a barrage of alerts and the inevitable alert fatigue.
We decided to start Openlayer to offer a more holistic solution that helps teams from the ML development and experiment tracking phase to more advanced tasks like monitoring and fairness. We established a strong baseline with this launch and are now building several features on top.
Stay tuned!