Neural Bits

Neural Bits

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One ugly truth on becoming an AI/ML Engineer!

A few Engineering & ML books I recommend. Video Courses, Resources and what works for me when reading and studying.
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Building MCP Servers for Video Search Engines
How MCP can solve the integration problem for multimodal search in Video RAG.
Cracking the Code to Multimodal AI Pipelines
Building efficient Multimodal Systems, unpacking Video, VLMs and Speech to Text (STT)
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Alex Razvant
11
Introducing Kubrick Course - A Multimodal Agent powered by MCP
Free course on building an AI Agent that understands Images, Audio and Video.
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Alex Razvant
2
Grow as an AI Engineer, past the surface level.
Three practical and in-depth resources to grow as an well-rounded AI Engineer!
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Alex Razvant
AI Educators are *lying* to you
A short reflection, AI hype, AI Experts and a big Surprise at the end!
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Alex Razvant
1
MCP is just a fancy API
Understand MCP as an Engineer. Build and debug one step-by-step in Python using FastMCP.
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Alex Razvant
Neural Bits
Neural Bits
Neural Bits specializes in AI/ML Engineering, sharing expert insights and hands-on advice for active and aspiring AI Builders. Join thousands of other engineers and learn how to build complex AI Systems, end-to-end.
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Code resources, implementations and projects.
Video Content
Courses, Tutorials and Insights in Video Format
Free Courses
Kubrick: An Multimodal AI Agent for Video Processing
Building a complete AI System for Video Processing, with Tool Calling Agents, MCP, LLMOps and production-ready software engineering practices in mind.
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Applied AI Systems

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Introducing Kubrick Course - A Multimodal Agent powered by MCP
Free course on building an AI Agent that understands Images, Audio and Video.
 • 
Alex Razvant
2
7 Frameworks and Toolkits you must know for AI and Deep Learning
Concise set of diagrams and summaries of popular NVIDIA libraries used by AI/ML Engineers across all ML, DL, and GenAI.
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Alex Razvant
Complete Overview in Vision AI 2025
From Pixels to Vision Language Models. Updated periodically.
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Alex Razvant
2
Building robust ML model pipelines in Triton deployments
Detach processing stages from Python to Triton Inference Server. Build a custom Python Backend and compose a Model Pipeline.
Part II - Deploying Ultralytics YOLO11 to Production Environments
Configuring, deploying and testing YOLO11 with Triton Inference Server. Using Supervision to draw detections on images.
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Alex Razvant
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