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.
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课程信息
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
对员工进行热门技能培训能否为您的公司带来益处?
体验 Coursera 企业版您将获得的技能
- Human-in-the-Loop Pipelines
- Distributed Model Training and Hyperparameter Tuning
- Cost Savings and Performance Improvements
- A/B Testing and Model Deployment
- Data Labeling at Scale
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
对员工进行热门技能培训能否为您的公司带来益处?
体验 Coursera 企业版授课大纲 - 您将从这门课程中学到什么
Week 1: Advanced model training, tuning and evaluation
Week 2: Advanced model deployment and monitoring
Week 3: Data labeling and human-in-the-loop pipelines
审阅
- 5 stars76.08%
- 4 stars17.39%
- 3 stars4.34%
- 2 stars2.17%
来自OPTIMIZE ML MODELS AND DEPLOY HUMAN-IN-THE-LOOP PIPELINES的热门评论
Online lab needs improvement. I kept disconnecting and I had to do the labs multiple times...
A complete Course + Certification + labs to master AWS Data science tools
Excellent platforms for advanced ML deployment AWS platforms!
Perfect. The vocareum labs are very helpful, whenever not understood in detail by some programming stuff. I will have a second look to them.
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