Building a Real-Time ML System. Together is a 188-lesson 48 hours 20 minutes self-paced course by Michael Guay. Ship a Real-Time ML System From Scratch Led by Michael Guay, this 188-lesson program is built around a single goal: taking a machine learning idea all the way to a deployed, scalable, real-time system using Python, Rust, LLMs, and…
Course facts
Lessons
188
Duration
48 hours 20 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Michael Guay
Price
Premium
Ship a Real-Time ML System From Scratch
Led by Michael Guay, this 188-lesson program is built around a single goal: taking a machine learning idea all the way to a deployed, scalable, real-time system using Python, Rust, LLMs, and Kubernetes.
What's Inside
150+ hours of recorded sessions drawn from four previous cohorts, so you can work through the material at your own pace
50 hours of live coding and practice built into each new cohort
Full source code for real projects, including a cryptocurrency price prediction system and a credit card fraud detection system
The Project
Past groups built a cryptocurrency price predictor; the current cohort works on a transaction fraud detection system, giving you a concrete pipeline to design, deploy, and scale rather than a set of disconnected demos.
What You Will Learn
You will practice building microservice architectures around real-time ML, apply a repeatable Feature → Training → Inference pipeline pattern, and work with the tooling that supports it in production: Kafka, a Feature Store, an Experiment Tracker, a Model Registry, and Kubernetes.
Who Should Join
This is built for ML engineers, data scientists, and developers who have already trained at least one model and want to move from theory into building functioning, deployable systems.
Additional
Who teaches Building a Real-Time ML System. Together? Michael Guay
Michael Guay is a US software engineer and prolific independent instructor publishing course material on the .NET / C# stack and the modern web frameworks adjacent to it.
The course catalog covers C# and .NET fundamentals, ASP.NET Core for back-end development, Entity Framework for data access, Blazor for full-stack C# web applications, plus the surrounding tooling and deployment patterns. The teaching style is patient and project-oriented, with each course typically building a working application end-to-end.
The CourseFlix listing under this source carries over 20 Michael Guay courses spanning that range. Material is paid and aimed at developers picking up the .NET stack or extending their existing .NET experience into newer parts of the platform.
What lessons are included in Building a Real-Time ML System. Together?
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Frequently asked questions
What prerequisites are needed before taking this course?
Participants should have a foundational understanding of machine learning, including experience in training at least one model. The course is designed for ML engineers, data scientists, and developers who are eager to transition from theoretical knowledge to practical application.
What projects will I work on during the course?
The course includes hands-on projects like a cryptocurrency price prediction system and a credit card fraud detection system. These projects provide real-world examples to practice designing, developing, and deploying real-time machine learning systems.
How does this course differ from other ML courses?
This course focuses on building end-to-end real-time machine learning systems with an emphasis on deployment and scalability using tools like Python, Rust, and Kubernetes. Unlike other courses that may stop at theory, this course includes practical projects and live coding sessions.
Which tools and platforms will be used in the course?
Participants will use Python, Rust, and Kubernetes to build microservice architectures with real-time ML capabilities. The course also involves using Kafka for data streaming and covers how to deploy these systems using Docker and GitHub Container Registry.
What topics are not covered in this course?
The course does not cover introductory machine learning concepts or basic programming skills. It assumes participants already have these foundational skills and focuses on the advanced aspects of real-time ML system deployment and scalability.
What is the expected time commitment for this course?
The course includes over 150 hours of recorded sessions and 50 hours of live coding, allowing participants to learn at their own pace. The time commitment will vary depending on the individual's learning pace and engagement with the live practice sessions.
How will the skills learned in this course benefit my career?
By mastering the development and deployment of real-time ML systems, participants will enhance their practical skills in building scalable applications. These skills are applicable in various industries, offering pathways to roles in advanced ML engineering and data science positions.
