hile you have hired top-notch data scientists to build models and invested in data science tools, your AI projects still may not be getting off the ground. Research shows that the share of AI models deemed “production worthy” but never put into production is anywhere between 50 and 90%. Do you see yourself on that spectrum? So, what are leaders and teams actually missing? What can they do to finally gain value out of AI?

The last mile for AI initiative success is the deployment and management of models in production that require new practices, skills, and technologies. This emerging area is called Machine Learning Operations, or MLOps, also referred to as ModelOps by leading analyst firms.

MLOps solves precisely for this challenge by bridging the gap between data and IT Ops teams, providing the capabilities that both teams need to work together to deploy, monitor, manage, and govern machine learning models in production. In this webinar, we provide insights into what MLOps is and how any organization can adopt it to finally derive value from machine learning projects.

During this session, you will learn:

• How to eliminate AI-related risks by adopting MLOps best practices
• The inherent challenges of production model deployment and how to overcome them
• Model monitoring best practices
• What production lifecycle management is and why it matters