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Slm Engineering End-To-End - rlscorner - 09-30-2026 ![]() Slm Engineering: End-To-End Released 8/2026 Taught by Dr. Sreedath Panat (MIT PhD) MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 36 Lessons ( 41h 41m ) | Size: 7.4 GB A 4-week live bootcamp taught by Dr. Sreedath Panat (MIT PhD). Take a small language model end to end, from raw data through tokenization, pretraining, fine-tuning, alignment, inference engineering, and deployment. Small models are the next enterprise standard. 67% of enterprises prefer on-premise AI. Open-source SLMs deliver 10x cost savings over cloud APIs. The shift from massive models to efficient, domain-specific ones is accelerating. Who is this for? Built for engineers who want to go deep. Engineers transitioning into LLM engineering, NLP, or enterprise AI Developers building AI-powered products - chatbots, search, document Q&A Engineers who want to go beyond API calls - to building and training models Researchers who need production engineering depth alongside theory What you will achieve Leave production-ready. Enterprise AI interview question "Design an enterprise SLM system that fine-tunes on private data, supports RAG with permission-aware retrieval, and deploys on-premise with sub-100ms latency. Walk me through the architecture." Asked at companies building enterprise AI products. You will have a complete answer. Build, train, and deploy enterprise SLMs from scratch - transformers, SFT, RLHF, quantization Design efficient training pipelines with LoRA, QLoRA, and distributed training Implement RAG, tool calling, and agentic workflows with small models Deploy models with vLLM, llama.cpp, and production inference servers Build industry-level portfolio projects from hands-on capstone work Homepage Code: https://slm-engineering.vizuara.ai/]https://slm-engineering.vizuara.ai/Quote:https://rapidgator.net/file/96053344bb1a6866bc12044e0a3d61c0/SLM_Engineering_End-to-End.part1.rar.html |