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：
deeplearning.ai
Amazon Web Services
Practical Data Science専門講座deeplearning.aiこのコースについて
12,830 最近の表示
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における3の3コース
上級レベル
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
約14時間で修了
英語
字幕：英語
習得するスキル
Human-in-the-Loop PipelinesDistributed Model Training and Hyperparameter TuningCost Savings and Performance ImprovementsA/B Testing and Model DeploymentData Labeling at Scale
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における3の3コース
上級レベル
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
約14時間で修了
英語
字幕：英語
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
5時間で修了
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時間で修了
8件のビデオ （合計47分）
5時間で修了
Week 2: Advanced model deployment and monitoring
Deploy models with A/B testing, monitor model performance, and detect drift from baseline metrics.
5時間で修了
10件のビデオ （合計62分）
5時間で修了
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時間で修了
7件のビデオ （合計41分）, 3 学習用教材, 3 個のテスト
レビュー
OPTIMIZE ML MODELS AND DEPLOY HUMAN-IN-THE-LOOP PIPELINES からの人気レビュー
LLにより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.
SHにより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! :)
AMにより2021年8月28日
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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