Chevron Left
Retour à Build, Train, and Deploy ML Pipelines using BERT

Avis et commentaires pour d'étudiants pour Build, Train, and Deploy ML Pipelines using BERT par deeplearning.ai

4.6
étoiles
71 évaluations
14 avis

À propos du cours

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....

Meilleurs avis

SL
5 juil. 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 juil. 2021

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

Filtrer par :

1 - 15 sur 15 Avis pour Build, Train, and Deploy ML Pipelines using BERT

par Israel T

19 juin 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.

par Pablo A B

5 juil. 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.

par Sneha L

6 juil. 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

par Magnus M

14 juin 2021

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

par Aleksa B

2 nov. 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.

par yugesh v

28 juil. 2021

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

par Ozma M

18 juil. 2021

EXcellent MLOps content, presentation, demo

par Tenzin T

7 sept. 2021

Highly recommended

par John S

6 oct. 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 ;-)

par Alexander M

22 juil. 2021

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

par Diego M

20 nov. 2021

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

par Mosleh M

6 août 2021

ok

par Mark P

13 sept. 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.

par Parag K

22 oct. 2021

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

par Clashing P

8 oct. 2021

hope there will be code implementation examples in the lectures