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Learn to Program: The Fundamentals
Unlocking Information Security I: From Cryptography to Buffer Overflows
Elements of AI
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AWS認定データエンジニア試験対策:データパイプライン実装、問題解決、最適化スキルを習得。13時間で試験トピックを網羅し、ハンズオン演習と模擬問題で実践的に学習。
AWS 데이터 엔지니어 자격증 시험 준비를 위한 6시간 과정. 데이터 수집, 변환, 저장, 운영, 보안 등 핵심 영역을 다루며 실습과 연습 문제로 실전 대비.
深入学习AWS数据工程技能,掌握数据管道实施、监控优化及安全管理。通过模拟试题和实践练习,全面备战AWS认证数据工程师考试。
AWS 데이터 엔지니어 자격증 시험 준비를 위한 종합 과정. 데이터 수집, 변환, 저장, 분석, 보안 등 핵심 영역을 다루며 실습과 모의고사를 통해 실전 대비 가능.
深入学习AWS数据工程技能,掌握数据管道实施、监控优化等核心能力。通过实践练习和模拟题,全面备战AWS认证数据工程师考试,提升职业竞争力。
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.
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