Бакалавриат 2022/2023

DevOps

Статус: Курс по выбору (Прикладная математика и информатика)

Направление: 01.03.02. Прикладная математика и информатика

Кто читает: Департамент больших данных и информационного поиска

Где читается: Факультет компьютерных наук

Когда читается: 4-й курс, 3 модуль

Формат изучения: с онлайн-курсом

Онлайн-часы: 32

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Поросенков Геннадий Андреевич

Язык: английский

Кредиты: 5

Контактные часы: 14

Course Syllabus

Abstract

DevOps is a set of software development practices that combine software development (Dev) and information-technology operations (Ops) to shorten the systems development life cycle, while frequently delivering features, fixes, and updates in close alignment with the given business objectives. Graduates often lack practical skills and experience required for professional success in the IT industry. The DevOps course will give you an opportunity to develop and polish relevant skills needed for large-scale complex projects, including system design, system deployment, support, version control systems, virtualization, etc. This valuable hands-on experience will allow you to start working on your own industrial-level projects and effectively collaborate with your team members if you are hired by an IT company. During the course, you will have an opportunity to solve many practical problems focused on various aspects of a product life cycle. In cooperation with МТС.Тета and МТС Cloud.

Learning Objectives

• To master the basic tools to develop and maintain software service lifecycle

Expected Learning Outcomes

• To know how to deploy cloud services
• To understand Git and build automation systems
• To understand the concept of CI / CD

Course Contents

• Introduction
• Virtualization and Cloud
• IaC and Configuration Management
• Containerization
• CI/CD

Assessment Elements

• Test 1
  5 questions with answers based on the materials of the lecture. Held at the 2nd lecture.
• Test 2
  5 questions with answers based on the materials of the lecture. Held at the 3d lecture.
• Test 3
  5 questions with answers based on the materials of the lecture. Held at the 4th lecture.
• Test 4
  5 questions with answers based on the materials of the lecture. Held at the 5th lecture.
• Test 5
  5 questions with answers based on the materials of the lecture. Held at the 6th lecture.
• Test 6
  5 questions with answers based on the materials of the lecture. Held at the 7th lecture.
• HW
  Issued after the 7th lecture.
• Mini-quiz
• Practical task

Interim Assessment

• 2022/2023 3rd module
  0.1 * Test 3 + 0.2 * Practical task + 0.1 * Test 5 + 0.2 * Mini-quiz + 0.1 * Test 4 + 0.1 * Test 6 + 0.1 * Test 1 + 0.1 * Test 2

Bibliography

Recommended Core Bibliography

• Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705

Recommended Additional Bibliography

• M Narasimha Murty, & V Susheela Devi. (2015). Introduction To Pattern Recognition And Machine Learning. World Scientific.