Learn to design, develop, deploy, and scale end-to-end real-time ML systems using Python, Rust, LLMs, and Kubernetes. This course offers a hands-on approach to mastering the technologies that power real-time machine learning applications.
Course Highlights
What awaits you in this comprehensive program:
150+ hours of recorded sessions from previous 4 cohorts, allowing you to learn at your own pace.
Access to complete source codes of projects, including a cryptocurrency price prediction system and a credit card fraud detection system, providing real-world examples for practice.
50 hours of live coding and practice for each cohort, ensuring a dynamic learning experience.
Course Overview
In this interactive practical course, participants will create a real-time machine learning system from scratch, covering deployment and scalability aspects. Past cohorts worked on a cryptocurrency price predictor, with the upcoming cohort focusing on a transaction fraud detection system.
Who Should Enroll?
This course is engineered for ML engineers, data scientists, and developers who possess a foundational understanding of machine learning—having trained at least one model—and are eager to advance from theoretical knowledge to practical application.
Key Learning Outcomes
Master the development of microservice architectures integrated with real-time ML capabilities.
Implement a robust universal approach: Feature → Training → Inference Pipeline.
Gain proficiency in leveraging modern tools such as Kafka, Feature Store, Experiment Tracker, Model Registry, and Kubernetes for efficient ML system operations.
Why Choose This Course?
This is not a theoretical course offering "passive learning" opportunities. It is an immersive experience where you will build functional systems, thereby significantly boosting your career in the tech industry.
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.
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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.
