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學生對 deeplearning.ai 提供的 Optimize ML Models and Deploy Human-in-the-Loop Pipelines 的評價和反饋

4.7
44 個評分
11 條評論

課程概述

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....

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LL
2021年7月21日

In this course I learn about training, fine-tuning, deploying and monitoring Models in AWS. The ideas about Human-in-the-loop pipelines is pretty cool.

SH
2021年9月14日

I have worked in data science field for some years, so make me easier to appreciate the contents prepared by course mentors. Thanks! :)

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1 - Optimize ML Models and Deploy Human-in-the-Loop Pipelines 的 11 個評論(共 11 個)

創建者 Chris D

2021年8月28日

This specialisation, including this course, has a comprehensive coverage of various practical considerations to build ML pipelines. Taking a data driven AI perspective and including data exploration, feature.

The labs are very well thought through and prepared. 

Basic understanding of AWS services (especially S3 and Cloudwatch) is required.

創建者 phoenix c

2021年9月12日

훌륭한 course 였습니다. 감사합니다.

다만 총 3달 코스 인데도 불구하고 마지막 달 코스 기간내 였는데 4달째 자동 결재가 되었습니다.

3달 코스를 4달 비용으로 자동으로 결재되는 부분은 문제가 있어 보입니다.

4달째 자동 결재부분은 자동결재가 되지 않도록 해결해줄수 있으실까요 ?

創建者 lonnie

2021年7月22日

In this course I learn about training, fine-tuning, deploying and monitoring Models in AWS. The ideas about Human-in-the-loop pipelines is pretty cool.

創建者 Simon h

2021年9月14日

I have worked in data science field for some years, so make me easier to appreciate the contents prepared by course mentors. Thanks! :)

創建者 Alexander M

2021年8月29日

E​xcellent course with ability to directly practice in Amazon SageMaker.

創建者 Kee K Y

2021年8月7日

Excellent platforms for advanced ML deployment AWS platforms!

創建者 Diego M

2021年11月20日

It is difficult to understand completely lab exercises . Very Nice course!!

創建者 Antony W

2021年8月17日

G​ood information...assignments are ok

創建者 Mark P

2021年9月13日

Coding exercises are a bit too structured, there isn't as much learning as I would have liked. That said, having the notebooks for reference at work is quite useful. Good introduction.

創建者 Parag K

2021年10月22日

Detailed code walk through explaining the code would have been helpful similar how it was done in Tensorflow In Practice Specalization

創建者 YANGYANG C

2021年9月4日

Introduce the ML workflow nicely, the assignment is not that hard and hope could have more explanation.