ModelOps: Establishing a Pipeline and Governance for AI at Scale [Video}

For those looking to maximize the ROI and strategic value of AI, scalability is everything. However, successfully scaling AI is anything but one-size-fits-all and rather an amalgamation of data, models, infrastructure, and applications – all of which can be scaled to varying degrees.

For example, 3 companies who have successfully scaled AI and are reaping major benefits are:

• The U.S. postal service. Leveraging OCR-powered handwriting recognition, the agency effectively interprets over 15 billion pieces of mail per year with 98% accuracy.
• Facebook. Has developed models so large they can be effectively used as ground truth for semi-supervised learning of new models
• YouTube. Processes over 500 hours of content each minute, automatically scanning user-submitted videos for inappropriate content and copyright/trademark violations

The secret to their success? ModelOps.

ModelOps is a governance, lifecycle model and mindset for operationalizing AI. The goal is to accelerate iteration, enabling rapid retuning, retraining, or rebuilding of AI models by providing an uninterrupted flow between the development, operationalization, and maintenance of models within AI-based systems. In addition, ModelOps also provides business leaders tangible insight into model performance and outcomes.

To shed light on what ModelOps is and how it can be used to increase the speed of AI deployment by 15X, we invited Seth Clark, Founder & Head of Product, Modzy to share his perspective at our recent Applied AI event.

Watch his full session,  ModelOps: Establishing a Pipeline and Governance for AI at Scale, here and now to discover:

• What it takes to scale AI in terms of data, models, infrastructure, and applications
• Best practices and key considerations for selecting the best-fit ModelOps tool
• Developing a strong people strategy to ensure applied AI success
• How to overcome current challenges with moving models from the lab to production environment
• How to increase collaboration between DS , ML engineering teams and the business with ModelOPs
• The growing importance of AI explainability and how it can be best addressed