Shifting Minds: Exploring OpenShift's AI Landscape
Jun 14, 2024
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Andy Grimes discusses OpenShift's AI Landscape with insights on MLOps, AI model development, and accelerating model deployment using OpenShift AI. The discussion covers local experimentation, governance tools for AI, and the collaboration between IBM and Red Hat in the AI space.
Open source models like Hugging Face and Olama enable diverse AI experimentation.
Tools like Watson X Governance aid in auditing and managing AI models for transparency and fairness.
OpenShift AI facilitates seamless model switching, MLOps pipelines, and resource provisioning for optimal AI performance.
Deep dives
Open Source Models Trending in AI Experimentation
Many organizations are leveraging open source models like Hugging Face and Olama for AI experimentation. The power of open source allows for diverse model selections and experimentation. However, as applications become more critical, there is a growing awareness of the liabilities associated with open source models.
Model Selection and Governance for Liability Mitigation
Organizations are increasingly concerned about bias and liabilities in AI models. The emergence of tools like Watson X Governance helps in auditing and managing models, ensuring transparency in the data used for training and the decision-making process. Open source frameworks like Open Data Hub provide a foundation for building AI models while maintaining governance standards.
Blue-Green Deployment for AI Models and MLOps with OpenShift AI
In the realm of AI model deployment, practices akin to blue-green deployments and A/B testing are gaining traction. Tools like OpenShift AI facilitate this by enabling seamless switching between different models for evaluation. Additionally, organizations are adopting MLOps pipelines, utilizing platforms like OpenShift AI to fine-tune, retrain, and deploy models effectively for optimal performance and bias mitigation.
OpenShift AI Simplifies Resource Provisioning in MLOps Pipelines
OpenShift AI streamlines resource provisioning within the MLOps pipeline, ensuring a consistent experience for data scientists across different projects. It automates code upgrades, providing a seamless process for managing code packages in the pipeline. With OpenShift's model, data scientists can access tools consistently, while data engineers benefit from scalable resources integrated with the application platform.
OpenShift AI's Comprehensive Approach to Data Science and Application Integration
OpenShift AI offers a comprehensive solution for data scientists to build, train, and deploy models, with seamless integration into the application development process. The platform enables easy model hosting and serving, allowing developers to access and utilize models efficiently. Additionally, OpenShift AI supports experimentation, hyperparameter tuning, and model training, promoting a collaborative environment for data science and application teams to work together effectively.
