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Updated in [June 30th, 2023]
This course, Build Train and Deploy ML Pipelines using BERT, is the second course in the Practical Data Science Specialization. It provides learners with the opportunity to learn how 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. Learners will be able to transform the dataset into BERT-readable features and store the features in the Amazon SageMaker Feature Store. They will also be able to 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, learners will be able to evaluate the model’s accuracy and only deploy the model if the accuracy exceeds a given threshold.
[Applications]
The application of this course is to enable data-focused developers, scientists, and analysts to build, train, and deploy scalable, end-to-end ML pipelines using Amazon SageMaker and Hugging Face’s highly-optimized implementation of the state-of-the-art BERT algorithm. This course provides the skills to effectively deploy data science projects and overcome challenges at each step of the ML workflow in the AWS cloud.
[Career Paths]
A career path that this course could lead to is a Machine Learning Engineer. Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models and pipelines. They must have a strong understanding of data science and machine learning algorithms, as well as the ability to develop and deploy ML pipelines in the cloud. They must also be able to work with a variety of data sources and formats, and be able to troubleshoot and optimize ML pipelines. As the demand for ML-driven solutions continues to grow, the demand for Machine Learning Engineers is expected to grow as well.
[Education Paths]
The recommended educational path for learners interested in building, training, and deploying ML pipelines using BERT is to pursue a degree in Data Science. This degree will provide learners with the necessary skills to develop and deploy data science projects in the cloud. The degree will cover topics such as data analysis, machine learning, data engineering, and cloud computing. Learners will also learn how to use various tools and technologies such as Python, SQL, Amazon SageMaker, and BERT to build, train, and deploy ML pipelines.
The development trend for data science degrees is to focus on the practical application of data science. This means that learners will be able to apply their knowledge to real-world problems and develop solutions that can be deployed in the cloud. Additionally, the degree will focus on the use of cloud computing and machine learning to develop and deploy ML pipelines. This will enable learners to develop and deploy ML pipelines quickly and efficiently.
