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Build, Train, and Deploy ML Pipelines using BERT(으)로 돌아가기

deeplearning.ai의 Build, Train, and Deploy ML Pipelines using BERT 학습자 리뷰 및 피드백

4.6
별점
79개의 평가
17개의 리뷰

강좌 소개

In the second course of the Practical Data Science Specialization, you will learn to automate a natural language processing task by building an end-to-end machine learning pipeline using Hugging Face’s highly-optimized implementation of the state-of-the-art BERT algorithm with Amazon SageMaker Pipelines. Your pipeline will first transform the dataset into BERT-readable features and store the features in the Amazon SageMaker Feature Store. It will then fine-tune a text classification model to the dataset using a Hugging Face pre-trained model, which has learned to understand the human language from millions of Wikipedia documents. Finally, your pipeline will evaluate the model’s accuracy and only deploy the model if the accuracy exceeds a given threshold. 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....

최상위 리뷰

SL
2021년 7월 5일

It is one of course with the exact content required for an working professional who is already working with AWS and want to leverage the benefits of sagemaker for their ML deployment tasks

YV
2021년 7월 27일

Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!

필터링 기준:

Build, Train, and Deploy ML Pipelines using BERT의 18개 리뷰 중 1~18

교육 기관: Israel T

2021년 6월 19일

Great for introduction to the AWS Sagemaker tools. But if you really want to dive deeper on the tools, you need to add and explore other resources, since most of the codes are already provided in the exercise.

교육 기관: Pablo A B

2021년 7월 5일

G​ives good general overview of Pipelines. However, assignments are way too easy, which makes them not to add too much to the learning.

교육 기관: Sneha L

2021년 7월 6일

It is one of course with the exact content required for an working professional who is already working with AWS and want to leverage the benefits of sagemaker for their ML deployment tasks

교육 기관: Magnus M

2021년 6월 14일

The videos are excellent. The labs are way too easy, just copying some variable names.

교육 기관: Aleksa B

2021년 11월 2일

Very good course. Highly recommended.

One thing that I would add is to go more in depth about certain concepts (like pipelines) and go through a bit more complex examples in practical exercises.

Overall good job, love it, thank you.

교육 기관: yugesh v

2021년 7월 28일

Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!

교육 기관: Ozma M

2021년 7월 18일

EXcellent MLOps content, presentation, demo

교육 기관: Anzor G

2021년 12월 27일

Great Course! Unlimited Thanks to you!

교육 기관: Tenzin T

2021년 9월 7일

Highly recommended

교육 기관: John S

2021년 10월 6일

This is NOT a course about BERT, it's a course about Amazon SageMaker ML Ops. I learned plenty of useful stuff about Amazon SageMaker, I learned nothing new about BERT. The content is a mixed bag - week 1 is poor quality, week 2 is good quality, week 3 is very good quality. The labs aren't great - trivial "fill-in the missing variable/term" style (which, ironically, can probably be done automatically by a BERT model nowadays ;-)

교육 기관: Alexander M

2021년 7월 22일

Week 3 lab gave twice error 'Failed' and 3rd time it went without an issue. This was quite frustrating. Overall, good class. Thx.

교육 기관: Diego M

2021년 11월 20일

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

교육 기관: Mosleh M

2021년 8월 6일

ok

교육 기관: Sanjay C

2022년 1월 17일

I was a little disappointed in the courses in this specialization - the issue is that a large part of the coding was already done. In order for this course to be an "advanced" level course, the students should be asked to write their own SQL/pandas/python code for database access and data processing.

교육 기관: Muneeb V

2021년 12월 14일

The lectures video are good but there are some issues with labs. It was taking time to load and the allotted time was less than the required time for the lab. Moreover, there were access denied issues in the lab code.

교육 기관: Mark P

2021년 9월 13일

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.

교육 기관: Parag K

2021년 10월 22일

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

교육 기관: Clashing P

2021년 10월 8일

hope there will be code implementation examples in the lectures