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AIM214-R1: [NEW LAUNCH!] Introducing Amazon SageMaker Studio, full IDE for ML

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Manage episode 276820446 series 2819993
Kandungan disediakan oleh John Rotenstein. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh John Rotenstein atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
Machine learning (ML) is a complex, iterative, often time-consuming process. One difficult aspect is the lack of integration between the workflow steps and the tools to accomplish them. Join us as we introduce Amazon SageMaker Studio, the first full integrated development environment (IDE) for ML that makes it easy to build, train, tune, debug, deploy, and monitor ML models at scale. It pulls together the ML workflow steps in a unified, visual interface-since they're performed and tracked within one environment, the non-linear and iterative nature of ML development is greatly simplified. You can quickly move between steps, compare results and adjust inputs and parameters, and iterate faster with Amazon SageMaker Studio.
  continue reading

520 episod

Artwork
iconKongsi
 
Manage episode 276820446 series 2819993
Kandungan disediakan oleh John Rotenstein. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh John Rotenstein atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
Machine learning (ML) is a complex, iterative, often time-consuming process. One difficult aspect is the lack of integration between the workflow steps and the tools to accomplish them. Join us as we introduce Amazon SageMaker Studio, the first full integrated development environment (IDE) for ML that makes it easy to build, train, tune, debug, deploy, and monitor ML models at scale. It pulls together the ML workflow steps in a unified, visual interface-since they're performed and tracked within one environment, the non-linear and iterative nature of ML development is greatly simplified. You can quickly move between steps, compare results and adjust inputs and parameters, and iterate faster with Amazon SageMaker Studio.
  continue reading

520 episod

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