Курсы Облачные технологии

In this course, you’ll learn about AWS’s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You’ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You’ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS.

Learn how to use the AWS Software Development Kit to develop secure and scalable cloud applications. You will be able to setup AWS SDK and developer
credentials for Java, C#/.NET, Python, and JavaScript. Explore how to interact with AWS services, and develop solutions by using the AWS SDK. Also learn about key concepts, best practices, and troubleshooting tips.

DevOps Engineering on AWS teaches you how to use the combination of DevOps cultural philosophies, practices, and tools to increase your organization’s ability to develop, deliver, and maintain applications and services at high velocity on AWS. This course covers Continuous Integration (CI), Continuous Delivery (CD), infrastructure as code, microservices, monitoring and logging, and communication and collaboration. Hands-on labs give you experience building and deploying AWS CloudFormation templates and CI/CD pipelines that build and deploy applications on Amazon Elastic Compute Cloud (Amazon EC2), serverless applications, and container-based applications. Labs for multi-pipeline workflows and pipelines that deploy to multiple environments are also included.

In this two-day course, you focus on learning the tools and skills necessary to troubleshoot VMware vSAN™ 7 implementations.

This course presents the knowledge and skills to successfully design a cloud infrastructure that supports multiple types of services. The course takes an “open” approach and outlines infrastructure, application platform, and cloud management platform as the fundamental layers in a cloud. It explores the business needs and challenges that have led the organization to adopt a cloud solution. This course highlights the benefits, challenges, and considerations of design choices and emphasizes the importance of proper requirements gathering in the design process. Additionally, students are provided with opportunities to practice these new skills while performing exercises and labs. This course prepares the student for the Dell Technologies Proven Professional Cloud Architect Specialist level certification exam

This three-day instructor-led course is intended for data professionals who want to design and implement database solutions in SQL Server and on Microsoft Azure.  Security, Availability, Disaster Recovery, Scalability, Monitoring and Troubleshooting options will also be demonstrated.

Migrating to AWS focuses on planning and migrating existing workloads to the AWS Cloud. The course covers various cloud migration strategies with a detailed discussion on each phase of the migration process, including portfolio discovery, application migration planning and design, migration execution, and post-migration validation and application optimization. This course is designed for Solutions Architects and Engineers who perform cloud migrations, have an understanding of core AWS services and design patterns covered in Architecting on AWS. This course is also available to IT project managers involved in the planning of those migrations who have completed AWS Technical Essentials.

In this course, you will learn about the process of planning and designing both relational and nonrelational AWS databases. It will teach you how to use workload requirements to define database design considerations and also explore the features and capabilities of the eight AWS database services. By the end of the course, you will be able to determine which AWS database service is right for your workloads, and design the database to meet your requirements.

In this intermediate-level course, 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.

Running Container-Enabled Microservices on AWS is designed to teach you how to manage and scale container-enabled applications by using Amazon Elastic Container Service (ECS). This course highlights the challenges of running containerized applications at scale and provides guidance on creating and using Amazon ECS to develop and deploy containerized microservices-based applications. In the hands-on lab exercises you will use Amazon ECS to handle long-running services, build and deploy container images, link services together, and scale capacity to meet demand. You will also learn how to run container workers for asynchronous application processes.