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.
提供方
deeplearning.ai
亚马逊网络服务系统
Practical Data Science 專項課程deeplearning.ai課程信息
11,807 次近期查看
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可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 3 門課程（共 3 門）
高級
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
完成時間大約為14 小時
英語（English）
字幕：英語（English）
您將獲得的技能
Human-in-the-Loop PipelinesDistributed Model Training and Hyperparameter TuningCost Savings and Performance ImprovementsA/B Testing and Model DeploymentData Labeling at Scale
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 3 門課程（共 3 門）
高級
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
完成時間大約為14 小時
英語（English）
字幕：英語（English）
提供方
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
亚马逊网络服务系统
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.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 5 小時
Week 1: Advanced model training, tuning and evaluation
Train, tune, and evaluate models using data-parallel and model-parallel strategies and automatic model tuning.
完成時間為 5 小時
8 個視頻 （總計 47 分鐘）
完成時間為 5 小時
Week 2: Advanced model deployment and monitoring
Deploy models with A/B testing, monitor model performance, and detect drift from baseline metrics.
完成時間為 5 小時
10 個視頻 （總計 62 分鐘）
完成時間為 5 小時
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.
完成時間為 5 小時
7 個視頻 （總計 41 分鐘）, 3 個閱讀材料, 3 個測驗
審閱
來自OPTIMIZE ML MODELS AND DEPLOY HUMAN-IN-THE-LOOP PIPELINES的熱門評論
由 LL 提供2021年7月21日
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.
由 SH 提供2021年9月14日
I have worked in data science field for some years, so make me easier to appreciate the contents prepared by course mentors. Thanks! :)
由 AM 提供2021年8月28日
Excellent course with ability to directly practice in Amazon SageMaker.
關於 Practical Data Science 專項課程
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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