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学生对 deeplearning.ai 提供的 Optimize ML Models and Deploy Human-in-the-Loop Pipelines 的评价和反馈

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课程概述

In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence. After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting. Lastly, you will set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost. The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud....

热门审阅

KK

Feb 15, 2022

Highly technical but beneficial course that allows you to explore resource constraints of an ML application. Thanks for simplifying as much as possible, enjoyed every bit!

MH

Mar 22, 2022

Perfect. The vocareum labs are very helpful, whenever not understood in detail by some programming stuff. I will have a second look to them.

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1 - Optimize ML Models and Deploy Human-in-the-Loop Pipelines 的 22 个评论（共 22 个）

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Aug 29, 2021

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Mar 31, 2022

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