Uploaded October 2025 | Updated September 2026, 2 weeks ago

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AI is becoming core knowledge in modern software development. Even if you don’t specialize in ML, you’ll be asked to build, integrate, or maintain AI features.

This video servers to explain 10 essential machine learning and AI concepts for developers: model parameters, quantization, embeddings & vector databases, retrieval augmented generation, inference, tokens & context windows, guardrails, function calling, memory, and cost/rate limits, so you can ship reliable, cost-efficient AI features with confidence.

Resources mentioned:
Blog: Model Parameters (2B vs 7B vs 40B) → https://travis.media/blog/ai-model-parameters-explained]
Blog: Quantization (4-bit, 8-bit, VRAM math) →https://travis.media/blog/ai-model-quantization-explained/
Tokenizer tool → platform.openai.com/tokenizer

Thanks Brilliant for sponsoring this video

Chapters
00:00 Intro
01:00 1 - Parameters
02:09 2- Quantization
03:24 3- Embeddings & Vector databases
04:52 Sponsor
06:10 4- RAG
07:28 5 - Inference
08:15 6 - Tokens & Context Windows
09:58 7 - Guardrails
11:01 8 - Function Calling
12:19 9 - Memory
13:17 10 - Cost & Rate Limits
14:15 Did you know all 10?

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