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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Offered By
Practical Data Science on the AWS Cloud SpecializationDeepLearning.AIAbout this Course
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Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
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100% online
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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. 14 hours to complete
English
Subtitles: English
Skills 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. 14 hours to complete
English
Subtitles: English
Offered by
DeepLearning.AI
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Amazon Web Services
Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 90 fully featured services for compute, storage, networking, database, analytics, application services, deployment, management, developer, mobile, Internet of Things (IoT), Artificial Intelligence, security, hybrid and enterprise applications, from 44 Availability Zones across 16 geographic regions. AWS services are trusted by millions of active customers around the world — including the fastest-growing startups, largest enterprises, and leading government agencies — to power their infrastructure, make them more agile, and lower costs.
Syllabus - What you will learn from this course
5 hours to complete
Week 1: Advanced model training, tuning and evaluation
Train, tune, and evaluate models using data-parallel and model-parallel strategies and automatic model tuning.
5 hours to complete
8 videos (Total 47 min)
5 hours to complete
Week 2: Advanced model deployment and monitoring
Deploy models with A/B testing, monitor model performance, and detect drift from baseline metrics.
5 hours to complete
10 videos (Total 62 min)
5 hours to complete
Week 3: Data labeling and human-in-the-loop pipelines
Label data at scale using private human workforces and build human-in-the-loop pipelines.
5 hours to complete
7 videos (Total 41 min), 3 readings, 3 quizzes
Reviews
- 5 stars77.21%
- 4 stars16.45%
- 3 stars5.06%
- 2 stars1.26%
TOP REVIEWS FROM OPTIMIZE ML MODELS AND DEPLOY HUMAN-IN-THE-LOOP PIPELINES
by YVJan 4, 2022
It's an awesome course to get a feel for A/B testing on cloud environment and Augmented AI
by LLJul 21, 2021
In this course I learn about training, fine-tuning, deploying and monitoring Models in AWS. The ideas about Human-in-the-loop pipelines is pretty cool.
by IKMay 12, 2022
Online lab needs improvement. I kept disconnecting and I had to do the labs multiple times...
by JHMay 26, 2022
Well organized. Videos correlate well with programming exercises.
About the Practical Data Science on the AWS Cloud Specialization
Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.
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