Live online course
MLOps foundations: ML System Design and Cloud-Native ML
Welcome to the MLOps basic course, an introduction to the latest approaches for managing the lifecycle of your ML projects. Join us to learn how to manage the deployment of ML models into real-world services.
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    Duration: 6 live sessions start coming soon
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    Price: €550 (€450 early bird rate until August 31)
    * Get reimbursed by your employer

This course is designed for ML engineers, data scientists, data engineers, DevOps, and software engineers

By enrolling, you will:
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    Figure out what are the core principles of MLOps
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    Learn how to properly start ML projects, design the ML architecture and define requirements
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    Get familiar with the Cloud-Native stack and how to use it for ML
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    Get hands-on experience with Docker and Kubernetes in the context of ML
6 live session
Immediate expert input
Real-world skills

About the course

Live online course suggests live coding and workshop sessions with real-time feedback from experts and colleagues. With each session, you get hands-on experience that can be immediately applied to your work.

We will learn how to plan ML model deployment and use the essential engineering tools - Docker and Kubernetes - to productionalize your ML models.

So, get ready to dive into the world of MLOps and start building your ML project today!

Introduction to the course
Course syllabus
Session 1

Getting started with MLOps

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coming soon
Course overview, course projects topics discussion
MLOps motivation and challenges
Overview of the main MLOps components
ML lifecycle
Session 2

ML System Design

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coming soon
Ideation phase of the ML project
How to approach ML System Design for your project
ML System Design artifacts
How to draw ML System diagrams
Session 3

MLOps tools and environment

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coming soon
Tooling for MLOps
MLOps environment setup
Microservice architecture in ML
Cloud-Native ML intro
Session 4

Docker and k8s for ML

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coming soon
Containerization, Docker
Orchestration, Kubernetes (k8s)
Kubernetes distributions and options for local development
How to use Docker/k8s stack for ML tasks
Session 5

Deep dive into Kubernetes for ML

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coming soon
Scale, monitor, and debug in k8s
K8s external access
Custom resources
Automatic model updates
Session 6

Demo day

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coming soon
Present team projects
Retrospective and follow-up questions
Lead ML Engineer

Meet the top expert

Dmytro Voitekh is a seasoned ML engineer with 8 years of experience in machine learning, MLOps, and full-stack engineering. He has a strong background in helping both early-stage startups and established tech companies to leverage ML features. His domains of expertise include machine learning and MLOps, and he has worked as an ML consultant, ML architect/lead, and CTO of a startup.


Dmytro has worked with a number of companies, including Proxet and GIPHY, and his extensive experience in the field makes him a valuable asset for any company that wants to incorporate machine learning into their business processes. He has numerous talks, workshops, and practical courses dedicated to ML, which showcases his expertise in the field.

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