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Zpět ke kurzu: Exam Readiness: AWS Certified Machine Learning - Specialty * Poslední termín:
The AWS Certified Machine Learning – Specialty exam validates a candidate s ability to design, implement, deploy, and maintain machine learning (ML) or deep learning (DL) solutions for given business problems. People with one to two years of experience developing, architecting, or running ML - DL workloads on the AWS cloud should join this workshop to lea> how to prepare to successfully pass the exam. The workshop explores the exam’s topic areas, shows how they relate to machine learning on A
The AWS Certified Machine Learning – Specialty exam validates a candidate s ability to design, implement, deploy, and maintain machine learning (ML) or deep learning (DL) solutions for given business problems. People with one to two years of experience developing, architecting, or running ML - DL ...
Audience This course is intended for: Machine learning practitioners preparing to take the AWS Certified Machine Learning – Specialty exam...
Goals This course is designed to teach you how to: Identify their strengths and weaknesses in each of the exam domains. Create a subsequent study plan to prepare for the exam....
Prerequisites We recommend that attendees of this course to have: One or two years of hands-on experience developing, architecting, or running ML - deep learning workloads on the AWS cloud. Proficiency at expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimization Understanding of complete ML pipeline and its components Experience with ML and deep learning frameworks Understanding and applying model training, deployment and operational best practices Course outline Module 0: Course Introduction Module 1: Exam Overview and Test-taking Strategies Exam overview, logistics, scoring, and user interface Question mechanics and design Test-taking strategies Module 2: Domain 1: Data Engineering Domain 1.1: Data Repositories for machine learning Domain 1.2: Identify and implement a data-ingestion solution Domain 1.3: Identify and implement a data-transformation solution Walkthrough of study questions Domain 1 quiz Module 3: Domain 2: Exploratory Data Analysis Domain 2.1: Sanitize and prepare data for modeling Domain 2.2: Perform featuring engineering Domain 2.3: Analyze and visualize data for ML Walkthrough of study questions Domain 2 quiz Module 4: Domain 3: Modeling Domain 3.1: Frame business problems as machine learning (ML) problems Domain 3.2: Select the appropriate model(s) for a given ML problem Domain 3.3: Train ML models Domain 3.4 Perform hyperparameter optimization Domain 3.5 Evaluate ML models Walkthrough of study questions Domain 3 quiz Module 5: ML Implementation and Operations Domain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance Domain 4.2: Recommend and implement the appropriate ML services and features for a given problem Domain 4.3: Apply basic AWS security practices to ML solutions Domain 4.4: Deploy and operationalize ML solutions Walkthrough of study questions Domain 4 quiz Module 6: Comprehensive study questions Module 7: Study Material Module 8: Wrap-up
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