Chevron Left
返回到 Build, Train, and Deploy ML Pipelines using BERT

学生对 提供的 Build, Train, and Deploy ML Pipelines using BERT 的评价和反馈

115 个评分


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....



Jul 5, 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


Jul 27, 2021

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


1 - Build, Train, and Deploy ML Pipelines using BERT 的 24 个评论(共 24 个)

创建者 Pablo A B

Jul 5, 2021

创建者 Sneha L

Jul 6, 2021

创建者 Israel T

Jun 19, 2021

创建者 Mark P

Sep 13, 2021

创建者 Magnus M

Jun 14, 2021

创建者 Aleksa B

Nov 2, 2021

创建者 yugesh v

Jul 28, 2021

创建者 RLee

Jul 28, 2022

创建者 Janzaib M

Apr 17, 2022

创建者 The M

Apr 24, 2022

创建者 Ozma M

Jul 18, 2021

创建者 Anzor G

Dec 27, 2021

创建者 Tenzin T

Sep 7, 2021

创建者 John S

Oct 6, 2021

创建者 学洲 刘

Feb 6, 2022

创建者 Alexander M

Jul 22, 2021

创建者 Diego M

Nov 20, 2021

创建者 Burhanudin B

Jun 3, 2022

创建者 Mosleh M

Aug 6, 2021

创建者 Sanjay C

Jan 17, 2022

创建者 Muneeb V

Dec 14, 2021

创建者 Parag K

Oct 22, 2021

创建者 Clashing P

Oct 8, 2021

创建者 Md. W A

Mar 27, 2022