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Volver a Build, Train, and Deploy ML Pipelines using BERT

Opiniones y comentarios de aprendices correspondientes a Build, Train, and Deploy ML Pipelines using BERT por parte de deeplearning.ai

4.6
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102 calificaciones

Acerca del Curso

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....

Principales reseñas

SL

5 de jul. de 2021

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

27 de jul. de 2021

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

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1 - 24 de 24 revisiones para Build, Train, and Deploy ML Pipelines using BERT

por Pablo A B

5 de jul. de 2021

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

por Sneha L

6 de jul. de 2021

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

por Israel T

19 de jun. de 2021

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.

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 Magnus M

14 de jun. de 2021

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

por Aleksa B

2 de nov. de 2021

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.

por yugesh v

28 de jul. de 2021

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

por RLee

28 de jul. de 2022

V​ery hands-on AWS BERT labs! Expecting more labs coming...

por Janzaib M

17 de abr. de 2022

Very Hands On Practical Information for the Industry

por The M

24 de abr. de 2022

Exactly the material I am looking for. Fabulous.

por Ozma M

18 de jul. de 2021

EXcellent MLOps content, presentation, demo

por Anzor G

27 de dic. de 2021

Great Course! Unlimited Thanks to you!

por Tenzin T

7 de sep. de 2021

Highly recommended

por John S

6 de oct. de 2021

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 ;-)

por 学洲 刘

6 de feb. de 2022

As a machine learning engineer i never met automl in my career before. This course shows me the power of automl. But the lab2 need too mucn time to traing the model, i hope the providers could add 2 hours in that assignment lab.

por Alexander M

22 de jul. de 2021

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

por Diego M

20 de nov. de 2021

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

por Burhanudin B

3 de jun. de 2022

This is amazing course

por Mosleh M

6 de ago. de 2021

ok

por Sanjay C

17 de ene. de 2022

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.

por Muneeb V

14 de dic. de 2021

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.

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 Clashing P

8 de oct. de 2021

hope there will be code implementation examples in the lectures

por Md. W A

27 de mar. de 2022

Unable to complete Practical Data Science Specialization because grading system does not work.