The State of Production MLOps in the Cloud Native Ecosystem

Explore the current state of production MLOps in the cloud native ecosystem through this comprehensive conference talk. Gain insights into the challenges faced in production machine learning and learn about key areas to focus on for reliable and scalable ML pipelines. Dive into essential principles, patterns, and frameworks powering various phases of the MLOps lifecycle, including model training, deployment, and monitoring. Discover best practices abstracted from real-world production use-cases of machine learning operations at scale. Learn how to leverage tools for deploying, explaining, securing, monitoring, and scaling production ML systems. Explore topics such as practical AI ethics, accountability structures, open-source libraries as policy, architectural blueprint convergence, evolving monitoring areas, data-centric approaches, robust DataOps/DataMesh evolution, end-to-end metadata interoperability, secure MLOps, and the shift from project-based to product-oriented mindsets. Understand the importance of mapping technical outputs to business outcomes and developing cross-functional capabilities by design. Gain valuable insights into organizational ratios and iterative growth strategies for successful MLOps implementation.

Intro
Model ende begins once trained
Consolidating Practical AI Ethics
Consolidating Accountability Struct.
AI Open Source Libraries as Policy
Architectural Blueprint Convergence
Converging Into Cannonical Stack(s)
Maturing Monitoring Areas in ML
Evolving to Observability By Design
From Model-Centric to Data-Centric
Robust DataOps/DataMesh Evolution
End to End Metadata Interoperability
Demand for Secure End to End MLOps
Mindset From Projects to Products
Map Tech Outputs to Biz Outcomes
Cross Func. Capabilities By Design
Exploring Organisational Ratios
Iterative Organisational Growth