Optimize ML Models and Deploy Human-in-the-Loop Pipelines

Overview

Discover how to enhance the efficiency of your machine learning models with the third installment of the Practical Data Science Specialization on Coursera. This course focuses on optimizing machine learning workflows through performance improvement, cost reduction, and the integration of human intelligence.

Learn how to fine-tune your text classifiers with Amazon SageMaker's Hyper-parameter Tuning, and deploy ambitious A/B testing to monitor real-time prediction performance between models. The course also introduces strategies to scale the more successful model effortlessly using Amazon SageMaker Hosting.

Moreover, the course teaches you how to set up a human-in-the-loop pipeline utilizing Amazon Augmented AI and Amazon SageMaker Ground Truth, aimed at correcting misclassifications and creating valuable new training data.

With in-depth exploration of Practical Data Science, you're prepared to handle large datasets from varied sources and leverage the scalability and flexibility of cloud environments—key aspects when dealing with extensive data science projects.

Offered by Coursera and designed for data-centric developers, scientists, and analysts who are familiar with Python and SQL, this specialization equips you with the necessary skills to build, train, and manage scalable ML pipelines. It's particularly suitable for those looking to implement both automated and human-enhanced processes within the AWS cloud.

Gain practical expertise and navigate the challenges of the machine learning workflow efficiently with this specialization geared towards Machine Learning and Amazon Web Services (AWS).

Taught by

Antje Barth, Shelbee Eigenbrode, Sireesha Muppala and Chris Fregly

Subjects

united states