Switch to English Site

dotsdots

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

描述

In the third course of the Practical Data Science Specialization, you will learn a series of performance-improvement and cost-reduction techniques to automatically tune model accuracy, compare prediction performance, and generate new training data with human intelligence.

After tuning your text classifier using Amazon SageMaker Hyper-parameter Tuning (HPT), you will deploy two model candidates into an A/B test to compare their real-time prediction performance and automatically scale the winning model using Amazon SageMaker Hosting. Lastly, you will set up a human-in-the-loop pipeline to fix misclassified predictions and generate new training data using Amazon Augmented AI and Amazon SageMaker Ground Truth.阅读更多.

此资源由附属合作伙伴提供。 如果您支付培训费用,我们可能会赚取佣金来支持该网站。

按照数据工作岗位排列职业相关性

Optimize ML Models and Deploy Human-in-the-Loop Pipelines 中涵盖的技术和工具与 数据科学家 招聘广告中的要求最为相似。

相似度得分(满分 100)

学习顺序

Optimize ML Models and Deploy Human-in-the-Loop Pipelines is a part of 一 structured learning path.

Coursera
DeepLearning.AI

3 Courses 3 Months

Practical Data Science