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Conference: KubeCon + CloudNativeCon Europe 2023
Authors: Diana Atanasova, Julius von Kohout
2023-04-21
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The presentation discusses security issues in Kubeflow pipelines and proposes solutions to address them.
- Rootless containers can solve the issue of containers running as root, but there is a limitation in building OCI containers rootless
- The controllers in Kubeflow pipelines run as cluster admin, which is a security risk
- Namespace sharing can also be a security risk as collaborators gain access to service accounts
- Solutions proposed include reducing the complexity of controllers and using reduced cluster roles
- The presentation highlights the progress made in Kubeflow security, such as authentication and machine-to-machine authentication
Conference: KubeCon + CloudNativeCon Europe 2023
Authors: Maciej Mazur, Andreea Munteanu
2023-04-20
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The presentation discusses the use of secure MLOps in the life science industry, with a focus on protecting patient privacy and complying with industry standards.
- Tokenization is used to protect patient privacy by changing personally identifiable information to a token based on a hardware security key.
- Strict confinement features of micro-kubernetes distribution are used to ensure tamper-proof tokenization.
- Confidential computing is used to expand local Kubernetes clusters in a safe way by creating a VM on a public cloud and utilizing open enclave and open source projects to configure the confidential compute and underlying hardware features.
- The benefits of using public clouds for research use cases are discussed, including the ability to spike up capacity when training a bigger model.
- The presentation emphasizes the importance of using secure MLOps to comply with industry standards and protect patient privacy.
Conference: KubeCon + CloudNativeCon North America 2022
Authors: Jihye Choi
2022-10-28
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The conference presentation discusses two technologies, Mig and GPUdirect RDMA, for efficient use of GPU resources in AI and HPC tasks. Mig allows for splitting one unit of GPU into multiple instances, while GPUdirect RDMA enables efficient distributed processing. The presentation includes a POC result for each technology and highlights some points to consider for Kubernetes testing.
- Mig technology allows for efficient use of GPU resources by splitting one unit of GPU into multiple instances
- GPUdirect RDMA enables efficient distributed processing for deep learning tasks
- POC results show that Mig technology is suitable for model development and inference tasks, while GPUdirect RDMA is suitable for larger scale tasks
- Points to consider for Kubernetes testing are discussed in the presentation
Conference: KubeCon + CloudNativeCon Europe 2022
Authors: Jose Navarro, Prayana Galih
2022-05-18
The adoption of MLOps practices and tooling by organizations has considerably reduced the pain points to productionise Machine Learning models. However, with the increase of the number of models available by a company to deploy, the diversity of frameworks used to train those models and the different infrastructure required to run each model, new challenges arise for Machine Learning Platform teams e.g: How can we deploy new models from the same or different frameworks concurrently? How can we improve throughput and optimize resource utilization in our serving infrastructure, especially GPUs? Cookpad ML Platform Engineers will talk in this session how Triton Inference Server, an open-source model serving tool from Nvidia, can simplify the process of model deployment and optimise the resource utilisation by efficiently supporting concurrent models on single GPU or CPU, and multi-GPU servers.Click here to view captioning/translation in the MeetingPlay platform!
Conference: KubeCon + CloudNativeCon North America 2021
Authors: Dylan Wilder Patterson, Haytham Abuelfutuh
2021-10-15
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The presentation discusses the differences between DevOps and ML Ops, and how Flight can help with ML Ops workflows.
- ML Ops has different requirements than DevOps, such as dealing with large amounts of data and longer processing times
- Flight is a platform that can help with ML Ops workflows, providing features such as composition, caching, and validation
- Flight can handle individual containers for task execution environments, but not yet streaming data
- Flight has been well-received by data scientists for its ease of use and out-of-the-box parallelism
Conference: Transform X 2021
Authors: Jack Guo, Anitha Vijayakumar, Vishnu Rachakonda, Oleg Avdeëv
2021-10-07
Hosted by MLOps Community. Panelist to be announced soon. Demetrios Brinkmann, founder of MLOPs.community leads a panel managing the increasing compute requirements of AI models, whilst striking the right balance between flexibility for experimentation and stability in production. As enterprises collect more training data, and in many cases label it with Scale AI, they face the challenge of their models growing in both size and compute complexity. Join this session to learn how companies can develop robust and maintainable pipelines to ensure that ML experimentation remains possible, despite increasing model sizes and longer training times. This session will also cover compute for lifecycle phases from experimentation to scaling (with Metaflow, TFX, etc.) pipelines that are ready to deploy to production, including via microservices.