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SLM Engineering: End– to– End
Free Download SLM Engineering: End– to– End
Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With SLM Engineering: End– to– End, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
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
https://slm-engineering.vizuara.ai/
Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me
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