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Revolutionizing Hybrid Computing w/ Matthew Shaxted

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Contenido proporcionado por Hammerspace. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Hammerspace o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

Welcome to Data Unchained, the podcast where we delve into the evolving world of decentralized data and workflows. Hosted by Molly Presley, this episode features a thought-provoking discussion with Matthew Shaxted, Co-Founder and CEO of Parallel Works, about the challenges and opportunities in hybrid and multi-cloud environments.


Key Highlights:

- The journey of Parallel Works: From HPC simulations to democratizing large-scale computing resources.

- The convergence of HPC and AI infrastructure—how organizations are adapting to GPU-heavy workflows.

- Overcoming decentralized data challenges: Solutions for application portability and cost-efficient workload management.

The evolution of AI-driven task placement for seamless resource optimization.

- Real-world insights into managing hybrid and multi-cloud workloads with cost controls and global namespaces.

- Matthew also introduces ACTIVATE, Parallel Works' next-gen hybrid multi-cloud platform, and shares exciting announcements for the future, including advancements in Kubernetes integration and benchmarking AI task placement.


Learn more about Parallel Works: https://parallel.works


@parallel-works


#dataunchained #DecentralizedData #HybridCloud #MultiCloud #HPC #AIWorkflows #ParallelWorks #DataManagement #CloudComputing #ArtificialIntelligence #DataInnovation #TechPodcast #BigData #MachineLearning #futureofai


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Creative Commons Attribution 3.0 Unported License

https://creativecommons.org/licenses/by/3.0/deed.en_US



Hosted on Acast. See acast.com/privacy for more information.

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78 episodios

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Manage episode 457774840 series 3375926
Contenido proporcionado por Hammerspace. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Hammerspace o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

Welcome to Data Unchained, the podcast where we delve into the evolving world of decentralized data and workflows. Hosted by Molly Presley, this episode features a thought-provoking discussion with Matthew Shaxted, Co-Founder and CEO of Parallel Works, about the challenges and opportunities in hybrid and multi-cloud environments.


Key Highlights:

- The journey of Parallel Works: From HPC simulations to democratizing large-scale computing resources.

- The convergence of HPC and AI infrastructure—how organizations are adapting to GPU-heavy workflows.

- Overcoming decentralized data challenges: Solutions for application portability and cost-efficient workload management.

The evolution of AI-driven task placement for seamless resource optimization.

- Real-world insights into managing hybrid and multi-cloud workloads with cost controls and global namespaces.

- Matthew also introduces ACTIVATE, Parallel Works' next-gen hybrid multi-cloud platform, and shares exciting announcements for the future, including advancements in Kubernetes integration and benchmarking AI task placement.


Learn more about Parallel Works: https://parallel.works


@parallel-works


#dataunchained #DecentralizedData #HybridCloud #MultiCloud #HPC #AIWorkflows #ParallelWorks #DataManagement #CloudComputing #ArtificialIntelligence #DataInnovation #TechPodcast #BigData #MachineLearning #futureofai


Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic

Music promoted by https://www.free-stock-music.com

Creative Commons Attribution 3.0 Unported License

https://creativecommons.org/licenses/by/3.0/deed.en_US



Hosted on Acast. See acast.com/privacy for more information.

  continue reading

78 episodios

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