Leveling Up Your Existing Machine Learning Applications
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Amazon Web Services784 тыс
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37 дней – 10 1500:13Опубликовано 5 июня 2019, 16:31
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Implementing machine learning functionality into your company’s application is immensely powerful, but that’s only the first step. When it comes to optimizing costs, throughput, and agility of your ML Ops pipeline, there are countless changes still to be made! We'll begin by covering what existing machine learning workflows look like in production, and progress through the different types of improvements that can be done to cut costs, quicken times between model updates, and have more robust deployments.
Implementing machine learning functionality into your company’s application is immensely powerful, but that’s only the first step. When it comes to optimizing costs, throughput, and agility of your ML Ops pipeline, there are countless changes still to be made! We'll begin by covering what existing machine learning workflows look like in production, and progress through the different types of improvements that can be done to cut costs, quicken times between model updates, and have more robust deployments.
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