Build a Reproducible ML Workflow with Kubeflow Pipelines
Learn to create reproducible machine learning workflows using Kubeflow Pipelines, enhancing efficiency and scalability in ML projects with Google's open-source platform.
DevOps in Data Science - What Works and What Doesn't
Explore challenges and solutions in implementing DevOps for data science projects, focusing on balancing ML engineers' needs with data scientists' desire for simplicity using Kubeflow and other tools.
Component Registry for Kubeflow Pipelines
Explore a component registry for Kubeflow Pipelines, enhancing ML workflow collaboration on Kubernetes. Learn how shared components can streamline common tasks and improve efficiency in machine learning projects.
A Deep Dive into Kubeflow Pipelines
Explore Kubeflow Pipelines' architecture and components for executing cloud-native ML workflows on Kubernetes. Gain insights into the platform's inner workings and integration of ML pipeline elements.
Defending Against Adversarial Model Attacks Using Kubeflow
Learn to protect AI models from adversarial attacks using Kubeflow. Explore threats, defenses, and practical implementations in this comprehensive presentation by IBM experts.
Democratization of ML Pipelines and Bringing ML Workflows to Heterogeneous Cloud Native Platforms
Explore advancements in ML pipeline portability using Intermediate Representation, enabling cross-platform compatibility and democratizing ML workflow development with enhanced features and registry protocols.
ML Ops Design Patterns with Kubeflow Pipelines
Explore ML Ops design patterns using Kubeflow Pipelines to transition from notebook exploration to production, addressing challenges and implementing effective solutions.
Enabling Multi-user Machine Learning Workflows for Kubeflow Pipelines
Explore multi-user workflows for Kubeflow Pipelines using Kubernetes-native solutions. Learn how to implement access control, authentication, and authorization for machine learning platforms.
Know Your Enemy - Mapping Security Risks Using Threat Matrix for Kubernetes
Explore Kubernetes security using the Threat Matrix, learn to map real-world attacks, measure security coverage, and compare with MITRE ATT&CK for comprehensive protection of containerized environments.
Defending Against Adversarial Model Attacks Using Kubeflow
Explore building robust AI pipelines against adversarial attacks using Kubeflow and LFAI Adversarial Robustness Toolbox. Learn to test ML model robustness in production with Kubeflow Serving and KNative eventing.
AIOps for CI with Kubeflow Pipelines
Explore AI4CI, an AIOps tool leveraging Kubeflow Pipelines to monitor builds, predict failures, and create interactive dashboards for improved CI/CD visibility and root cause analysis.
Jupyter Extension for Executing Kubeflow Pipeline Seamlessly
Explore Elyra's integration with Kubeflow 2.0, enhancing ML workflow development and deployment on Kubernetes with visual editing and multi-language support.