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. 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. 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. Top 100 AWS Certified Cloud Practitioner Exam Preparation Questions. Concerns over bias in tech have heightened during 2020, with the Black Lives Matter protests forcing companies into pulling their AI facial. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. Tags ML model predictions and biases with Amazon SageMaker Clarify. Amazon SageMaker Clarify is a newly-launched service that allows AWS customers to detect bias in machine learning models, and raise transparency by explaining model behaviour to customers.
Next, you will work with Amazon SageMaker BlazingText, a highly optimized and scalable implementation of the popular FastText algorithm, to train a text classifier with very little code. You will then perform automated machine learning (AutoML) to automatically train, tune, and deploy the best text-classification algorithm for the given dataset using Amazon SageMaker Autopilot. SageMaker Autopilot now generates a model explainability report via SageMaker Clarify, the Amazon tool used to detect algorithmic bias while increasing the transparency of machine learning models. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier. Amazon Web Services is adding an AI explainability reporting feature to its SageMaker machine learning model builder aimed at improving model accuracy.
In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. In this first video, I show how you to use the bias detection capability in Amazon SageMaker Clarify, using bias metrics computed on a credit dataset, and on a.