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.
This course is part of the Practical Data Science on the AWS Cloud Specialization
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Practical Data Science on the AWS Cloud SpecializationDeepLearning.AIAbout this Course
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Flexible deadlines
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Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 3 in the
Advanced Level
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Approx. 10 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessSkills you will gain
- 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
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 3 in the
Advanced Level
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Approx. 10 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
Syllabus - What you will learn from this course
4 hours to complete
Week 1: Advanced model training, tuning and evaluation
4 hours to complete
8 videos (Total 47 min), 1 reading, 4 quizzes
4 hours to complete
Week 2: Advanced model deployment and monitoring
4 hours to complete
10 videos (Total 62 min)
4 hours to complete
Week 3: Data labeling and human-in-the-loop pipelines
4 hours to complete
7 videos (Total 41 min), 3 readings, 3 quizzes
Reviews
- 5 stars75.26%
- 4 stars18.27%
- 3 stars4.30%
- 2 stars2.15%
TOP REVIEWS FROM OPTIMIZE ML MODELS AND DEPLOY HUMAN-IN-THE-LOOP PIPELINES
by IKMay 12, 2022
Online lab needs improvement. I kept disconnecting and I had to do the labs multiple times...
by KYAug 6, 2021
Excellent platforms for advanced ML deployment AWS platforms!
by SHSep 14, 2021
I have worked in data science field for some years, so make me easier to appreciate the contents prepared by course mentors. Thanks! :)
by MDAug 17, 2022
Could've been better but the Labs are a bit too easy.
About the Practical Data Science on the AWS Cloud Specialization
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