МОВС MLOps (2023-24 уч. год, 2 модуль)

О курсе

The MLOps course covers a wide range of topics essential for effectively managing and deploying machine learning projects. It begins with an introduction to MLOps, providing an overview of principles and best practices. Students gain hands-on experience in creating ML projects using Python and learn about containerization using Docker. Code management and version control using Git servers are emphasized, enabling collaborative development. The course explores Continuous Integration and Continuous Deployment (CI/CD) techniques for automating the ML workflow. Data management, including storage, versioning, and management using tools like DVC, is covered. Students learn about experiment logging to track and analyze model performance. The course addresses computational power requirements, cloud-based solutions, and transferring learning to the cloud. Additional topics include knn indices, embeddings quantification, crowdsourcing, data labeling using platforms like Toloka, and an overview of the Amazon SageMaker stack. Talks by students and external lecturers provide practical insights. By course completion, students gain a comprehensive understanding of MLOps principles and practical skills for managing and deploying ML projects.

Занятия проводятся в Zoom по ... в ...

Контакты

Чат курса в TG: chat link

Преподаватель: Гончаренко Владислав, Dzen.ru

Ассистенты

Ассистент	Контакты
Владислав Наумов	@vlad21naumov
Алексей Макарчук	@alexmak123
	[1]

Материалы курса

Плейлист курса на YouTube: https://www.youtube.com/playlist?list=PLmA-1xX7IuzCRcPf_aGos3d2uvAQrd46c

GitHub с материалами курса: GitHub repository

Занятие	Тема	Дата	Ссылки
1 Запись	Ноутбук Intro to MLOps. Python Project Setup, Dependency Management
2 Запись	Ноутбук Code Management and VCS. Code Quality Tools and CI/CD
3 Запись	Ноутбук Data Management. DVC Setup
4 Запись	Ноутбук Logging for Experiments and Production. MLFlow Setup
5 Запись	Ноутбук Computing Power & Clouds. Moving to Remote Queued Runs
6 Запись	Ноутбук Production Hacks in ML. Practicing kNN Indexes and Quantization
7 Запись	Ноутбук Effective Inference

Формула оценивания

Оценка = Среднее за домашние задания

Домашние задания

1. Poetry Setup
2. Code Quality Tools Setup
3. DVC Setup
4. MLFlow Setup
5. Running on Remote Machine
6. ...

Литература

• "Machine Learning Engineering" by Andriy Burkov, 2020
• "Practical MLOps: Operationalizing Machine Learning Models (2021)", Noah Gift & Alfredo Deza