MLOps & ML System Design For Beginners

Описание: Most students know Python and TensorFlow, but 0% know how to ship production-grade models. If you can train a model but can't deploy it reliably, you won't get hired. This video is Part-1 of our Machine Learning System Design series, designed to bridge the gap between Jupyter Notebooks and Real-World Engineering.

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In this 5-hour comprehensive course, we move beyond "accuracy scores" and focus on the 90% of Machine Learning that tutorials skip: The System.

📚 IN THIS VIDEO (SYSTEM DESIGN PART 1)
In this 5-hour comprehensive course, we move beyond "accuracy scores" and focus on the 90% of Machine Learning that tutorials skip: The System.

We cover the 5 Core Principles of ML System Design:

Problem Definition: When not to use Machine Learning.
Design Thinking: Why most ML projects fail before a single line of code is written.
Data Strategy: Why better data beats better models every time.
Feature Engineering: Focusing on features over complex algorithms.
Complexity Management: Starting simple and scaling effectively.

⏱️ TIMESTAMPS
Will update

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Available Modules on Codex:

✅ Mathematics for Machine Learning (Linear Algebra, Stats, Probability)
✅ Core Machine Learning (Part 1)
✅ Practical MLOps (Implementation)
✅ COMING SOON: Agentic AI, GenAI, and Soft Skills Training

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⚠️ DISCLAIMER
This course is free. I do not charge for the Modern AI Stack on Codex. My focus is on my company, Second Brain Labs, and this content is created to help the community build better AI systems.

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