Practical Data Science on the AWS Cloud is a comprehensive specialization designed for data-focused developers, scientists, and analysts familiar with Python and SQL. It provides the practical skills to effectively deploy data science projects using Amazon SageMaker. The course covers preparing data, detecting statistical biases, feature engineering at scale, and training, evaluating, and tuning models with AutoML.
The specialization also delves into building, training, and deploying ML pipelines using BERT, managing machine learning features with a feature store, and debugging, profiling, tuning, and evaluating models while tracking data lineage and model artifacts. Additionally, it explores optimizing ML models and deploying human-in-the-loop pipelines to improve model performance with human intelligence.
Throughout the 10-week program, participants gain hands-on experience with cutting-edge algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker.
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The course covers preparing and training ML models using AutoML, building and deploying ML pipelines using BERT, and optimizing ML models and deploying human-in-the-loop pipelines for improved performance.
The first module focuses on analyzing datasets and training ML models using AutoML. Participants will learn to prepare data, detect statistical biases, and perform feature engineering at scale using pre-built algorithms.
The second module dives into building, training, and deploying ML pipelines using BERT. It covers storing and managing machine learning features using a feature store, debugging, profiling, tuning, and evaluating models while tracking data lineage and model artifacts.
The third module focuses on optimizing ML models and deploying human-in-the-loop pipelines to improve performance. Participants will learn performance-improvement and cost-reduction techniques, as well as setting up human-in-the-loop pipelines to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth.
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