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Build, Train, and Deploy ML Pipelines using BERT

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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.

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Embark on a transformative journey in the realm of Data Science & AI with the "Build, Train, and Deploy ML Pipelines using BERT" course. This advanced program, part of the Practical Data Science Specialization, is meticulously designed to enhance your skills in automating natural language processing tasks through the development of end-to-end machine learning pipelines using the cutting-edge BERT algorithm and Amazon SageMaker Pipelines. Guided by the expertise of Coursera, this course dives deep into the process of transforming datasets into BERT-readable features and storing them in the Amazon SageMaker Feature Store. You'll gain hands-on experience fine-tuning a text classification model utilizing a pre-trained Hugging Face model, which has been trained on an extensive corpus of Wikipedia documents. The course also emphasizes model evaluation, ensuring deployment only occurs when a specified accuracy threshold is met. Aimed at data-focused developers, scientists, and analysts, this course assumes a solid foundation in Python and SQL programming languages. It is perfect for those looking to master the deployment of scalable, end-to-end ML pipelines in the AWS cloud, leveraging the benefits of cloud scalability, agility, and cost-efficiency. With a comprehensive duration of 600 minutes, learners can choose from Starter and Professional subscription options to best suit their needs. Elevate your data science projects and overcome ML workflow challenges with this expert-level course, and become proficient in deploying robust machine learning solutions in the cloud.