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Opiniones y comentarios de aprendices correspondientes a Optimize ML Models and Deploy Human-in-the-Loop Pipelines por parte de deeplearning.ai

4.7
estrellas
44 calificaciones
11 reseña

Acerca del Curso

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....

Principales reseñas

LL
21 de jul. de 2021

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
14 de sep. de 2021

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 - 11 de 11 revisiones para Optimize ML Models and Deploy Human-in-the-Loop Pipelines

por Chris D

28 de ago. de 2021

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.

por phoenix c

12 de sep. de 2021

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 ?

por lonnie

22 de jul. de 2021

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.

por Simon h

14 de sep. de 2021

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

por Alexander M

29 de ago. de 2021

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

por Kee K Y

7 de ago. de 2021

Excellent platforms for advanced ML deployment AWS platforms!

por Diego M

20 de nov. de 2021

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

por Antony W

17 de ago. de 2021

G​ood information...assignments are ok

por Mark P

13 de sep. de 2021

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.

por Parag K

22 de oct. de 2021

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

por YANGYANG C

4 de sep. de 2021

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