Free Download AI On-Prem Deployment By Sander van VugtMP4 |
Video: h264, 1280x720 |
Audio: AAC, 44.1 KHz
Language: English |
Size: 959 MB |
Duration: 4h 50m
Master onpremises GenAI: deploy; optimize; and finetune LLMs on Linux and Kubernetes to deliver private; highperformance AI; without sending your data to the cloud.
Deploy private GenAI on your own infrastructure: Understand GenAI fundamentals and core components, choose the right LLM, and run models locally for control over data, privacy, performance, and cost.
Build and optimize on Linux: Configure GPU drivers and container GPU support, tune CPU/GPU inference, and run inference servers (llama.cpp and vLLM) on platforms with or without GPU, with an accessible client experience using OpenWebUI.
Production-ready workflows with Kubernetes: Add proprietary documents using RAG, prepare and run GenAI on Kubernetes (GPU workloads and non-GPU, storage, access, scaling/monitoring), and complete an end-to-end deployment that ties everything together.
Concise Overview
AI On-Prem Deployment is a hands-on video course from expert trainer Sander van Vugt that shows IT and DevOps professionals how to run generative AI on their own infrastructure, so theyre not dependent on external services beyond their own hardware. Instead of sending prompts and proprietary content to the cloud, youll learn how local deployment gives you control over data, privacy, performance, models, and predictable costs, which is especially important for regulated or critical environments, airgapped or restricted networks, and situations with limited connectivity.
The course starts with GenAI fundamentals such as core components, LLM basics, and strategies to reduce system Requirements, and then moves into practical deployment on Linux covering GPU drivers, container GPU support, CPU/GPU tuning, and inference servers that run in environments without GPUs. Youll also learn how to bring private data to your LLM using approaches such as RAG, and how to run GenAI on Kubernetes with a capstone end-to-end demonstration that ties everything together, including deploying an LLM, connecting to its API, adding private data, querying from a client, and applying best practices.
Skill Level
Beginner
Intermediate
Course Requirements
No course requirements
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