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.
Ce cours fait partie de la Spécialisation Practical Data Science
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Offert par
deeplearning.ai
Amazon Web Services
Spécialisation Practical Data Sciencedeeplearning.aiÀ propos de ce cours
20 221 consultations récentes
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Niveau avancé
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Approx. 11 heures pour terminer
Anglais
Sous-titres : Anglais
Compétences que vous acquerrez
Human-in-the-Loop PipelinesDistributed Model Training and Hyperparameter TuningCost Savings and Performance ImprovementsA/B Testing and Model DeploymentData Labeling at Scale
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Niveau avancé
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Approx. 11 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
Week 1: Advanced model training, tuning and evaluation
Train, tune, and evaluate models using data-parallel and model-parallel strategies and automatic model tuning.
4 heures pour terminer
8 vidéos (Total 47 min)
4 heures pour terminer
Week 2: Advanced model deployment and monitoring
Deploy models with A/B testing, monitor model performance, and detect drift from baseline metrics.
4 heures pour terminer
10 vidéos (Total 62 min)
4 heures pour terminer
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.
4 heures pour terminer
7 vidéos (Total 41 min), 3 lectures, 2 quiz
Avis
Meilleurs avis pour OPTIMIZE ML MODELS AND DEPLOY HUMAN-IN-THE-LOOP PIPELINES
par LL21 juil. 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.
par SH14 sept. 2021
I have worked in data science field for some years, so make me easier to appreciate the contents prepared by course mentors. Thanks! :)
par AM28 août 2021
Excellent course with ability to directly practice in Amazon SageMaker.
À propos du Spécialisation Practical Data Science
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.
Foire Aux Questions
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