According to Gartner’s predictions, “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization”.
MLOps allows the scaling of those ML models from the local “wizard”; developer to take advantage of processing in a clustered environment, all the while ensuring the data and models are reusable across the business entity.
In this webinar we will cover the following topics:
What is MLOps?
• Processes
• Deployment
• Scaling
Why do we need MLOps?
• Large disparate datasets
• Changing requirements for data
• Feature Modeling
• Monitoring
• Debugging
• Updating Models
MLOps Lifecycle
• Scoping
• Data Engineering
• Modeling
• Deployment
• Monitoring
Real-world Example(s)
