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.
Este curso forma parte de Programa especializado: Practical Data Science on the AWS Cloud
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Acerca de este Curso
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Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel avanzado
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Habilidades que obtendrás
- 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
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel avanzado
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
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.
Programa - Qué aprenderás en este curso
5 horas para completar
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 horas para completar
8 videos (Total 47 minutos)
5 horas para completar
Week 2: Advanced model deployment and monitoring
Deploy models with A/B testing, monitor model performance, and detect drift from baseline metrics.
5 horas para completar
10 videos (Total 62 minutos)
5 horas para completar
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 horas para completar
7 videos (Total 41 minutos), 3 lecturas, 3 cuestionarios
Reseñas
- 5 stars77,21 %
- 4 stars16,45 %
- 3 stars5,06 %
- 2 stars1,26 %
Principales reseñas sobre OPTIMIZE ML MODELS AND DEPLOY HUMAN-IN-THE-LOOP PIPELINES
por YV4 de ene. de 2022
It's an awesome course to get a feel for A/B testing on cloud environment and Augmented AI
por IK12 de may. de 2022
Online lab needs improvement. I kept disconnecting and I had to do the labs multiple times...
por MH22 de mar. de 2022
Perfect. The vocareum labs are very helpful, whenever not understood in detail by some programming stuff. I will have a second look to them.
por LL21 de jul. de 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.
Acerca de Programa especializado: Practical Data Science on the AWS Cloud
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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