AI/ML model training is becoming more time consuming due to the increase in data needed to achieve higher accuracy levels. This is compounded by growing business expectations to frequently re-train and tune models as new data is available. The two combined is resulting in heavier compute demands for AI/ML applications. This trend is set to continue and is leading data center companies to prepare for greater compute and memory-intensive loads for AI. Getting the right hardware and configuration can overcome these challenges. In this webinar, you will learn: - Kubeflow and AI workload automation - System architecture optimized for AI/ML - Choices to balance system architecture, budget, IT staff time and staff training. - Software tools to support the chosen system architecture

Hora

18:00 - 19:00 hs GMT+1

Organizador

Ubuntu and Canonical
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Control de seguridad
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  • 5 Steps to Security Validation

    20:00 - 21:00 hs GMT+1

    Fecha
    Lun 15-Jun-2020, 20:00 - 21:00 hs GMT+1
    Descripción
    Organizations have been managing security based on assumptions, hopes and best guesses for decades. We assume our technology will detect, block and send alerts, we hope our incident response techniques will be efficient and effective when under assault, and we (...)
    Cerrar
Mar 16 de Junio de 2020
Mié 17 de Junio de 2020