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Aprende a mejorar el rendimiento y disponibilidad de aplicaciones con Amazon CloudWatch. Explora métricas, crea alarmas y supervisa recursos en entornos locales, híbridos o en la nube de AWS.
Aprenda a monitorar e otimizar recursos e aplicativos na AWS com o Amazon CloudWatch. Explore métricas, logs, alarmes e testes sintéticos para melhorar desempenho e disponibilidade.
Learn to query and visualize property graph datasets in Neptune using Athena Connector and QuickSight. Hands-on experience with AWS services for graph database management and data visualization.
Hands-on lab for enabling AWS X-Ray to visualize and monitor application usage, creating service maps, and analyzing end-to-end requests in a microservices architecture.
Explore techniques to enhance foundation model performance: Retrieval Augmented Generation and fine-tuning. Learn AWS services, agent roles, evaluation methods, and data preparation for optimizing AI models.
Master prompt engineering fundamentals, techniques, and best practices. Learn to craft effective prompts, explore zero-shot and few-shot methods, and identify potential risks in this comprehensive introduction.
Explore the generative AI lifecycle, from defining use cases to deploying solutions. Learn to select, improve, and evaluate foundation models for effective AI application development.
Learn the machine learning lifecycle, AWS services for each stage, model sources, performance evaluation, and MLOps fundamentals to streamline ML project development and deployment.
Learn responsible AI practices, including core dimensions, AWS tools, model selection, data preparation, and transparent/explainable models. Gain insights into ethical AI development and human-centered design principles.
Learn data transformation techniques for ML, including cleaning, encoding, and feature engineering, using AWS services like SageMaker and Glue to prepare and optimize datasets.
Learn data validation strategies, bias mitigation, and data preparation techniques for machine learning using AWS services like SageMaker Clarify, Glue DataBrew, and Glue Data Quality.
Learn data fundamentals, AWS storage services, and data ingestion techniques for machine learning. Explore data types, visualization, and effective storage decisions for ML tasks using Amazon S3, Kinesis, and other AWS tools.
Build no-code ML and generative AI models on AWS using SageMaker Canvas. Learn data preparation, model training, evaluation, and deployment for tabular, time series, and text data without coding experience.
深入探讨AWS云安全基础,涵盖访问控制、数据加密、网络保护等核心概念。学习如何利用AWS安全服务满足企业需求,掌握云环境下的安全最佳实践。
Learn to build no-code machine learning models and use generative AI on AWS. Prepare data, train models, and leverage foundation models for text generation, summarization, and chat using Amazon SageMaker Canvas.
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