Webinar On-Demand: Bring Value to AI projects and Operationalize Data Insights

Companies across all industries have been struggling to give their customers an immersive and seamless experience. It’s no surprise when you see corporations dumping money into technologies leveraging ML models. However, it can be difficult to weigh returns from data insights and put ML projects into production. For companies to operationalize their data insights they need to acquire the skills needed for leveraging the available technologies that will accomplish this task at scale and at a lower cost.

In this Webinar, Dipika Jain and Nina Zumel discuss the topic of ModelOps, deploying ML models, and other technical deep dives, such as:

• The key challenges with MLOps
• How to operationalize ML projects
• How to deploy ML models in a repeatable, scalable process
• Monitoring model performance
• Keeping the models up and running and scaling for the future

We discuss the importance of data scientists and ML engineers to tackle challenges for a seamless transition and lower manual time. Covering model observability, detecting distribution drift and detecting data drifts. Touching on model explainability in SHAP (Shapley Additive Explanations) for relationship analysis between feature values and predictions.

If you’d like to explore the platform and improve your ML deployment check out our Community Edition. It allows users to experiment with the different deployment features and monitoring models in production.