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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

4.7
estrellas
57 calificaciones
9 reseña

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 - 10 de 10 revisiones para Build, Train, and Deploy ML Pipelines using BERT

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 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 Magnus M

14 de jun. de 2021

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

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 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 Ozma M

18 de jul. de 2021

EXcellent MLOps content, presentation, demo

por Tenzin T

7 de sep. de 2021

Highly recommended

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 Mosleh M

6 de ago. de 2021

ok

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.