Build, Train, and Deploy ML Pipelines using BERT

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Master ML Pipelines with BERT on Coursera:

Delve into the world of machine learning with the Practical Data Science Specialization's second course offered by Coursera. This hands-on course empowers you to automate natural language processing tasks seamlessly using the advanced BERT algorithm, courtesy of Hugging Face's optimized implementation, and Amazon SageMaker Pipelines.

You'll start by transforming datasets into BERT-friendly formats for efficient processing in the Amazon SageMaker Feature Store.

Following data preparation, the course guides you through the intricate process of fine-tuning text classification models using Hugging Face’s pre-trained models. These models are adept at understanding nuanced human language, thanks to extensive training on vast Wikipedia datasets.

Not stopping at model training, this course takes you further into evaluating your model's performance, ensuring deployment only surpasses specific accuracy thresholds.

Moreover, the course is an excellent fit for those aiming to manage large, complex datasets originating from diverse sources, offering insights into the practical aspects of data science. It underscores the benefits of cloud computing in data science, particularly the agility and cost-effectiveness of scaling projects within the AWS ecosystem.

Designed for developers, scientists, and analysts with a grounding in Python and SQL, this specialization prepares you to build, train, and deploy scalable ML pipelines in AWS efficiently.

Whether you're looking to automate processes or incorporate human-in-the-loop systems, this course equips you to tackle real-world challenges in deploying data science projects successfully. Provider:

Coursera. Categories:

Machine Learning Courses, Amazon Web Services (AWS) Courses, BERT Courses.

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Enseigné par

Antje Barth, Shelbee Eigenbrode, Sireesha Muppala and Chris Fregly

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