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.
提供:

このコースについて
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
習得するスキル
- 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
提供:

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.
シラバス - 本コースの学習内容
Week 1: Advanced model training, tuning and evaluation
Train, tune, and evaluate models using data-parallel and model-parallel strategies and automatic model tuning.
Week 2: Advanced model deployment and monitoring
Deploy models with A/B testing, monitor model performance, and detect drift from baseline metrics.
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.
レビュー
- 5 stars78.37%
- 4 stars14.86%
- 3 stars5.40%
- 2 stars1.35%
OPTIMIZE ML MODELS AND DEPLOY HUMAN-IN-THE-LOOP PIPELINES からの人気レビュー
It's an awesome course to get a feel for A/B testing on cloud environment and Augmented AI
Perfect. The vocareum labs are very helpful, whenever not understood in detail by some programming stuff. I will have a second look to them.
Online lab needs improvement. I kept disconnecting and I had to do the labs multiple times...
Excellent course with ability to directly practice in Amazon SageMaker.
Practical Data Science専門講座について
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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