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Empowering Enterprises: OPEA, AI, and the Future of Storage

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Contenuto fornito da open.intel. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da open.intel o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

In this episode, Daniel Valdivia, an engineer from MinIO, discusses his participation at KubeCon and his work in Kubernetes integrations and AI initiatives. We discussed the significance of object storage standardization via the Open Platform for Enterprise AI (OPEA), emphasizing the flexibility and scalability of MinIO's offerings. Daniel highlights MinIO's contributions to open source projects like PyTorch and Spark and shares insights on new hardware technologies like PCIe Gen 5. Daniel also announces the launch of MinIO's new AI store, designed to empower enterprises to efficiently manage exascale infrastructure and AI pipelines.

00:00 Introduction
00:13 Meet Daniel Valdivia: Engineer at Minio
00:24 The Importance of Kubernetes Integrations
00:43 Intel's Open Platform for Enterprise AI
00:58 MinIO's Unique Object Storage Solutions
01:56 Community Participation and Contributions
02:18 Ensuring Compatibility with AI Hardware
03:20 The Role of OPEA in Enterprise AI
05:56 Open Source Contributions and Challenges
09:12 Future of AI and Hardware Innovations
13:23 Big Announcement
14:40 Conclusion and Final Thoughts

Guest: Daniel Valdivia is an engineer with MinIO where he focuses on Kubernetes, ML/AI and VMware. Prior to joining MinIO, Daniel was the Head of Machine Learning for Espressive. Daniel has held senior application development roles with ServiceNow, Oracle and Freescale. Daniel holds a Bachelor of Engineering from Tecnológico de Monterrey, Campus Guadalajara and Bachelor of Science in Computer Engineering from Instituto Tecnológico y de Estudios Superiores de Monterrey.

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89 episodi

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Manage episode 454983777 series 3446189
Contenuto fornito da open.intel. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da open.intel o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

In this episode, Daniel Valdivia, an engineer from MinIO, discusses his participation at KubeCon and his work in Kubernetes integrations and AI initiatives. We discussed the significance of object storage standardization via the Open Platform for Enterprise AI (OPEA), emphasizing the flexibility and scalability of MinIO's offerings. Daniel highlights MinIO's contributions to open source projects like PyTorch and Spark and shares insights on new hardware technologies like PCIe Gen 5. Daniel also announces the launch of MinIO's new AI store, designed to empower enterprises to efficiently manage exascale infrastructure and AI pipelines.

00:00 Introduction
00:13 Meet Daniel Valdivia: Engineer at Minio
00:24 The Importance of Kubernetes Integrations
00:43 Intel's Open Platform for Enterprise AI
00:58 MinIO's Unique Object Storage Solutions
01:56 Community Participation and Contributions
02:18 Ensuring Compatibility with AI Hardware
03:20 The Role of OPEA in Enterprise AI
05:56 Open Source Contributions and Challenges
09:12 Future of AI and Hardware Innovations
13:23 Big Announcement
14:40 Conclusion and Final Thoughts

Guest: Daniel Valdivia is an engineer with MinIO where he focuses on Kubernetes, ML/AI and VMware. Prior to joining MinIO, Daniel was the Head of Machine Learning for Espressive. Daniel has held senior application development roles with ServiceNow, Oracle and Freescale. Daniel holds a Bachelor of Engineering from Tecnológico de Monterrey, Campus Guadalajara and Bachelor of Science in Computer Engineering from Instituto Tecnológico y de Estudios Superiores de Monterrey.

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89 episodi

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