The Rise of MLOps Monitoring

MLOps, or DevOps for ML, is a burgeoning enterprise area to help Data Science (DS) and IT teams accelerate the ML lifecycle of model development and deployment. Model training, the first step, is central to model development and now widely available on Jupyter Notebooks or with automated training (AutoML). But ML is not the easiest … Continue reading “The Rise of MLOps Monitoring”

AI Explained Video Series: The AI Concepts You Need to Understand

As businesses recognize the need for enhanced digital capabilities and build out more robust and advanced Data Science teams, AI is one of the areas being most heavily invested in. It is viewed within many organizations as a potential panacea: How will I forecast demand, make business recommendations, or combat customer churn? AI. How will … Continue reading “AI Explained Video Series: The AI Concepts You Need to Understand”

How to Detect Model Drift in ML Monitoring

AI adoption is rapidly rising across industries. With the advent of Covid-19, digital adoption by consumers and businesses has vaulted five years forward in a matter of eight weeks. However, the complexity of deploying ML has hindered the success of AI systems. MLOps and specifically the productionizing of ML models come with challenges similar to … Continue reading “How to Detect Model Drift in ML Monitoring”

[Video] AI Explained: What are Integrated Gradients?

We started a video series with quick, short snippets of information on Explainability and Explainable AI. The second in this series is on Integrated Gradients – more about this method and its applications. Learn more in the ~10min video below. The first in the series is one on Shapley values – watch that here. What … Continue reading “[Video] AI Explained: What are Integrated Gradients?”

Webinar: Why monitoring is critical to successful AI deployments

Even as AI provides significant benefits – like creating new revenue opportunities, increasing productivity, decreasing costs, and fostering innovation in outdated business models – there is a big potential for unintentional errors from outcomes that are unclear, biased and non-compliant. Companies often realize AI and ML performance issues after the damage has been done, which … Continue reading “Webinar: Why monitoring is critical to successful AI deployments”