AWS

This course is intended to provide solutions architects with the foundational knowledge required to successfully plan and perform lift and shift migrations to the AWS Cloud. In this course you will learn about methodologies for discovering, planning, performing, and tracking migrations by using various AWS tools and services.

In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.

In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage,and processing components of the analytics pipeline.You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost managementbest practices tothe operation of Amazon Redshift.

In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures.

This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning experience or knowledge will benefit from this course. Basicknowledge of Statistics will be helpful.

In this course, you will learn the benefits of cloud computing and how a cloud strategy can help you meet your business objectives. It explores the advantages of cloud computing and the fundamentals of AWS, including financial benefits. This course also introduces compliance and security concepts to help you consider the AWS platform within your cloud computing strategy.

In this course, you will build a data visualization solution using Amazon Quick Sight. Quick Sight allows everyone in your organization to understand your data by exploring through interactive dashboards, asking questions in natural language, or automatically looking for patterns and outliers powered by machine learning. This course focuses on connecting to data sources, building visuals, designing interactivity, and creating calculations. You will learn how to apply security best practices to your analyses. You will also explore the machine-learning capabilities built into Quick Sight.

You will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use case includes customer retention analysis to inform customer loyalty programs.

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.

Security is foundational to AWS. Governance at scale is a new concept for automating cloud governance that can help companies retire manual processes in account management, budget enforcement, and security and compliance. By automating common challenges, companies can scale without inhibiting agility, speed, or innovation. In addition, they can provide decision-makers with the visibility, control, and governance necessary to protect sensitive data and systems. In this course, you will learn how to facilitate developer speed and agility, and incorporate preventive and detective controls. By the end of this course, you will be able to apply governance best practices.

Currently, the average cost of a security breach can be upwards of $4 million. AWS Security Best Practices provides an overview of some of the industry best practices for using AWS security and control types. This course helps you understand your responsibilities while providing valuable guidelines for how to keep your workload safe and secure. You will learn how to secure your network infrastructure using sound design options. You will also learn how you can harden your compute resources and manage them securely. Finally, by understanding AWS monitoring and alerting, you can detect and alert on suspicious events to help you quickly begin the response process in the event of a potential compromise.

In this course, you will learn best practices for designing and using cloud-based video workflows. It covers important concepts related to video processing and delivery, the variables that can impact migration decisions, and real-world examples of hybrid and cloud use cases for AWS Media Services. It also introduces security, machine learning, and analytics concepts to help you consider how AWS Media Services fit into your overall cloud strategy.

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML)models. The course stresses the importance of data, model,and code to successful ML deployments. It will demonstrate the use of tools, automation, processes,and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators. The instructor willencourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and instructors.

This interactive course provides a deep dive into Amazon Web Services (AWS) best practices to help you perform effective and efficient AWS Well-Architected Framework Reviews. The course covers the phases of a review, including how to prepare, run, and get guidance after a review has been performed. Attendees should have familiarity with the AWS concepts, terminology, services, and tools that are covered in the intermediate, 200-level AWS Well-Architected Best Practices. This course provides an AWS Well-Architected Framework Review simulation and instructor-led group exercises and discussions regarding prioritizing and solution risks. The content focuses on teaching learners how to prepare proposals on high and medium-risk issues using the AWS Well-Architected Tool.

The Well-Architected Framework enables you to make informed decisions about your customers architectures in a cloud-native way and understand the impact of design decisions that are made. By using the Well-Architected Framework, you will understand the risks in your architecture and ways to mitigate them. This course is designed to provide a deep dive into the AWS Well-Architected Framework and its 6 pillars. This course also covers the Well-Architected Review process, and using the AWS Well-Architected Tool to complete reviews

This course gives developers exposure to and practices with best practices for building serverless applications using AWS Lambda and other services in the AWS serverless platform. You will use AWS frameworks to deploy a serverless application in hands-on labs that progress from simpler to more complex topics. You will use AWS documentation throughout the course to develop authentic methods for learning and problem-solving beyond the classroom.

This Certification & Training Course includes: Day 1 to 3: Architecting on AWS Objectives: Develop foundational knowledge about AWS and learn how to build resilient architectures. From topics coverage, will be the same as current 3 days Architecting on AWS course Day 4: Exam Readiness: AWS Certified Solutions Architect – Associate Objectives: Learn about the exam and how to answer the questions From topics coverage, will be the same as current 0.5 day Certification Readiness Workshop but there will be additional AWS services to be discussed and Qualtrics quiz 1. Day 5: Official AWS practice test Objectives: Test your knowledge to see if you are ready to take the exam See some real questions from the certification team and there will be Qualtrics quiz 2. Course wrap-up.

AWS Security Essentials covers fundamental AWS cloud security concepts, including AWS access control, data encryption methods, and how network access to your AWS infrastructure can be secured. Based on the AWS Shared Security Model, you learn where you are responsible for implementing security in the AWS Cloud and what security-oriented services are available to you and why and how the security services can help meet the security needs of your organization. This course enables you to dive deep, ask questions, work through solutions, and get feedback from AWS-accredited instructors with deep technical knowledge. This is a fundamental level course and is a part of the AWS Security learning path.