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The AI field is booming, but most roadmaps focus on theory over practice. This comprehensive guide provides a practical pathway for software engineers to become AI engineers in 2025 without needing deep ML expertise. Unlike traditional ML roles, AI engineering focuses on building functional AI systems with existing LLMs rather than training models from scratch. You'll learn core skills like prompt engineering, RAG systems, agentic workflows, and evaluation techniques, plus advanced topics like fine-tuning and self-hosting. The roadmap progresses from foundation prerequisites through specialization areas including knowledge management systems, multi-agent architectures, and monitoring techniques. Perfect for developers ready to build AI systems that solve real-world problems.
Building upon our [initial OCR vs VLM benchmarking study](https://www.dataunboxed.io/blog/ocr-vs-vlm-ocr-naive-benchmarking-accuracy-for-scanned-documents), this follow-up investigation tests the practical reality of self-hosted VLM deployment. While Part 1 established that Bigger commercial VLMs significantly outperform traditional OCR methods in accuracy, Part 2 addresses the critical question: Can quantized Qwen 2.5 VL models and tiny VLMs deliver production-ready OCR performance with reasonable hardware constraints?
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