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
102 évaluations

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 - 24 sur 24 Avis pour Build, Train, and Deploy ML Pipelines using BERT

par Pablo A B

5 juil. 2021

par Sneha L

6 juil. 2021

par Israel T

19 juin 2021

par Mark P

13 sept. 2021

par Magnus M

14 juin 2021

par Aleksa B

2 nov. 2021

par yugesh v

28 juil. 2021

par RLee

28 juil. 2022

par Janzaib M

17 avr. 2022

par The M

24 avr. 2022

par Ozma M

18 juil. 2021

par Anzor G

27 déc. 2021

par Tenzin T

7 sept. 2021

par John S

6 oct. 2021

par 学洲 刘

6 févr. 2022

par Alexander M

22 juil. 2021

par Diego M

20 nov. 2021

par Burhanudin B

3 juin 2022

par Mosleh M

6 août 2021

par Sanjay C

17 janv. 2022

par Muneeb V

14 déc. 2021

par Parag K

22 oct. 2021

par Clashing P

8 oct. 2021

par Md. W A

27 mars 2022