MLOps - Production & Delivery for ML Platforms

Machine learning operations (MLOps) is an emerging field that aims to address the challenges of deploying and managing machine learning models in production environments. It encompasses a wide range of practices and tools that facilitate the end-to-end machine learning lifecycle, from data preparation and model development to deployment, monitoring, and maintenance.

The MLOps landscape is rapidly evolving, driven by advances in machine learning algorithms, software engineering practices, and cloud computing infrastructure. In this conference, we will explore the latest trends, best practices, and case studies in MLOps, and discuss how they can be applied to improve the reliability, scalability, and efficiency of machine learning systems.
Topics covered in the track will include:

• Data management and governance for machine learning
• Model development and testing methodologies
• Continuous integration and deployment for machine learning
• Monitoring and alerting for machine learning systems
• Automated model retraining and versioning
• Collaboration and communication in MLOps teams
• Ethics and fairness in machine learning operations

Whether you are a data scientist, machine learning engineer, or software developer this track will provide valuable insights and practical advice for building and managing machine learning systems at scale.