Exam Readiness: AWS Certified Machine Learning – Specialty

This course prepares you to take the AWS Certified Machine Learning – Specialty exam, which validates your ability to design, implement, deploy, and maintain machine learning (ML) solutions. In this course, you’ll learn about the logistics of the exam and the mechanics of exam questions, and you’ll explore the exam’s technical domains. You’ll review core AWS services and key concepts for the exam domains: 1) Data Engineering 2)Exploratory Data Analysis 3)Modeling 4) Machine Learning Implementation and Operations You’ll also learn key test-taking strategies and will put them into action, taking multiple study questions. Once you’ve honed your skills, you’ll have the chance to take a quiz that will help you assess your areas of strength and weakness, so that you’ll know what to emphasize in your pre-exam studies. Course objectives: By the end of this course, you will be able to: •Identify your strengths and weaknesses in each exam domain so that you know what to focus on when studying for the exam •Describe the technical topics and concepts that make up each of the exam domains •Summarize the logistics and mechanics of the exam and its questions •Use effective strategies for studying and taking the exam Intended audience: This course is intended for: •ML practitioners who have at least one year of practical experience, and who are preparing to take the AWS Certified Machine Learning – Specialty exam Prerequisites: We recommend that attendees of this course have: •Proficiency expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimization •Understanding of the ML pipeline and its components •Experience with ML and deep learning frameworks •Understanding of and experience in model training, deployment, and operational best practices [Enroll] (www.aws.training) 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 ML •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 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: Domain 4 – 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: Additional Study Questions: •Opportunity to take additional study questions Module 7: Recommended Study Material: •Links to AWS blogs, documentation, FAQs, and other recommended study material for the exam Module 8: Course Wrap-up: •How to sign up for the exam •Course summary •Course feedback

Exam Readiness: AWS Certified Security – Specialty

The AWS Certified Security Specialty exam validates technical skills and experience in securing and hardening workloads and architectures on the AWS platform. Attendees with two or more years of hands-on experience designing and deploying cloud architecture on AWS should join this course to learn how to prepare and succeed in the exam. We will help you prepare for the exam by exploring the exam’s topic areas and mapping them to specific areas to study. We will review sample exam questions in each topic area, teaching you how to interpret the concepts being tested so that you can better eliminate incorrect responses. •Course level: Advanced •Duration: 2 hours Course objectives: In this course, you will learn to: •Navigate the logistics of the examination process •Understand the exam structure and question types •Identify how questions relate to AWS security best practices •Interpret the concepts being tested by an exam question •Allocate your time spent studying for the AWS Certified Security Specialty exam Intended audience: This course is intended for: • Individuals who perform a security role Prerequisites: We recommend that attendees of this course have: •Minimum of five years of IT security experience, designing and implementing security solutions •At least two years of hands-on experience securing AWS workloads, and security controls for workloads on AWS  Course outline: Module 1: Course Introduction: •Exam logistics •Exam mechanics Module 2: Exam Domains: •Incident response •Logging and monitoring •Infrastructure security •Identity and access management •Data protection Module 3: Wrap Up: •Course summary •Course assessment

Best Practices for Data Warehousing with Amazon Redshift

In this course, you will learn about the concepts of implementing a data warehouse using Amazon Redshift. You will learn about basic table design, data storage, data ingestion techniques, and workload management. You will also learn about the effect of node and cluster sizing.

Deep Dive on Container Security

Security should be the first concern for any project – maintaining the confidentiality, integrity and availability of your architecture. Containers present a unique middle ground between full instance management and pure services. Bertram Dorn, AWS Security Specialist will help you learn how to achieve segregation, control access, organize namespaces, manage memory, secure communications as well as how to create the corresponding risk assessment.