Optimize ML Models and Deploy Human-in-the-Loop Pipelines
- Offered byCoursera
Optimize ML Models and Deploy Human-in-the-Loop Pipelines at Coursera Overview
Duration | 11 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Optimize ML Models and Deploy Human-in-the-Loop Pipelines at Coursera Highlights
- 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 Practical Data Science Specialization
- Advanced Level Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
- Approx. 11 hours to complete
- English Subtitles: English
Optimize ML Models and Deploy Human-in-the-Loop Pipelines at Coursera Course details
- 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.
- Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost.
- The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.
Optimize ML Models and Deploy Human-in-the-Loop Pipelines at Coursera Curriculum
Week 1: Advanced model training, tuning and evaluation
Course 3 Overview
Week 1 Outline
Advanced Model Training and Tuning
Tune a BERT-based Text Classifier
Checkpointing
Distributed Training Strategies
Custom Algorithms with Amazon SageMaker
Week 1 Summary
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Week 1
Week 2: Advanced model deployment and monitoring
Week 2 Outline
Model Deployment Overview
Model Deployment Strategies
Amazon SageMaker Hosting: Real-Time Inference
Amazon SageMaker: Real-time Inference Production Variants
Amazon SageMaker Batch Transform: Batch Inference
Model Integration
Monitoring ML Workloads
Model Monitoring using Amazon SageMaker Model Monitor
Week 2 Summary
Week 2
Week 3: Data labeling and human-in-the-loop pipelines
Week 3 Outline
Data Labeling
Data Labeling with Amazon SageMaker Ground Truth
Data Labeling Best Practices
Human-In-The-Loop Pipelines
Human-In-The-Loop Pipelines with Amazon Augmented AI (Amazon A2I))
Week 3 Summary
Course 3 Optional References
Acknowledgements
Become a Deeplearning.AI Mentor/Tester
Week 3
Optimize ML Models and Deploy Human-in-the-Loop Pipelines at Coursera Faculty details
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