
Name
                                    MLOps: Deploying Your Machine Learning Models
                                        Description
                                    With the emergence of deep learning integrated into today's software products, a modern software development process needs an effective way to automatically test and deploy machine learning models as part of continuous delivery. In this presentation, Coveros CEO Jeffery Payne introduces deep learning AI models and how to effectively test and deploy them as part of product development. Approaches to testing models will be discussed as well as how to automate your deployment process when machine learning is part of your product. Steps toward a mature MLOps process will be discussed to help participants begin improving their MLOps process.
Speakers
                                    
                                
Date & Time
                                    Tuesday, July 23, 2024, 9:00 AM - 10:15 AM
                                        Location Name
                                    Texas 2-3
                                        Session Type
                                    Talk
                                        Track
                                    Technology for All
                                        Learning Level
                                    Learning
                                        Learning Objectives
                                    1. Attendees will understand what MLOps is all about
2. Attendees will learn the business challenges MLOps addresses
3. Attendees will gain a working knowledge of the array of open source tools available to automate your MLOps process
4. Attendees will understand what an end-to-end MLOps process looks like
                                        2. Attendees will learn the business challenges MLOps addresses
3. Attendees will gain a working knowledge of the array of open source tools available to automate your MLOps process
4. Attendees will understand what an end-to-end MLOps process looks like
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