| Bonus Resources.txt | 102.4 B | ||
| Exercise Files | |||
| demo-01-Responses API | |||
| demo-01-Responses API.sh | 2.6 KB | ||
| demo-02-ResponsesAPI.ipynb | 55.1 KB | ||
| ipynb_checkpoints | |||
| demo-02-PromptEngineering | |||
| demo-02-PromptEngineering.sh | 7 KB | ||
| demo-03-ImplementingARAGSolution | |||
| demo-03-ImplementingARAGSolution.sh | 3.6 KB | ||
| pdfs | |||
| AquaSentry_DS12_Manual.pdf | 3.9 KB | ||
| PercoWave_PW750_Manual.pdf | 4 KB | ||
| demo-04-FineTuningAModel | |||
| demo-04-FineTuningAModel.sh | 7.7 KB | ||
| support-tone-training.jsonl | 2.8 KB | ||
| demo-02-ResponsesAPI-checkpoint.ipynb | 55.1 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 01. Introduction | |||
| 01. Evolution of LLMs.mp4 | 8.6 MB | ||
| 01. Evolution of LLMs.srt | 8 KB | ||
| 02. 1. LLMs, Model Selection, and Application Planning | |||
| 01. Big picture How do LLMs work.mp4 | 8.4 MB | ||
| 01. Big picture How do LLMs work.srt | 7.6 KB | ||
| 02. Proprietary models vs. open-source models.mp4 | 9.3 MB | ||
| 02. Proprietary models vs. open-source models.srt | 9 KB | ||
| 03. 2. Architecture and Components of LLMs | |||
| 01. Introducing the transformer architecture.mp4 | 11.1 MB | ||
| 01. Introducing the transformer architecture.srt | 10.6 KB | ||
| 02. How do models represent text.mp4 | 9.4 MB | ||
| 02. How do models represent text.srt | 9.4 KB | ||
| 03. Prompts and model generation parameters.mp4 | 9.7 MB | ||
| 03. Prompts and model generation parameters.srt | 9.8 KB | ||
| 04. 3. Building LLM Applications | |||
| 01. Deploy and query a model hosted in Microsoft Foundry.mp4 | 13.2 MB | ||
| 01. Deploy and query a model hosted in Microsoft Foundry.srt | 7.7 KB | ||
| 02. Manage roles and conversation state with the Responses API.mp4 | 11.4 MB | ||
| 02. Manage roles and conversation state with the Responses API.srt | 8.2 KB | ||
| 03. Streaming responses, reasoning effort, and summary.mp4 | 7 MB | ||
| 03. Streaming responses, reasoning effort, and summary.srt | 3.9 KB | ||
| 05. 4. Prompt Design for LLM Applications | |||
| 01. Adapt LLMs for your applications.mp4 | 2 MB | ||
| 01. Adapt LLMs for your applications.srt | 2.2 KB | ||
| 02. Prompt design for LLMs.mp4 | 7.4 MB | ||
| 02. Prompt design for LLMs.srt | 7.4 KB | ||
| 03. Anatomy and components of a prompt.mp4 | 6.3 MB | ||
| 03. Anatomy and components of a prompt.srt | 3.4 KB | ||
| 04. Prompt design techniques.mp4 | 15.1 MB | ||
| 04. Prompt design techniques.srt | 7.6 KB | ||
| 06. 5. Grounding LLMs with Retrieval-Augmented Generation (RAG) | |||
| 01. The need for model grounding.mp4 | 5.8 MB | ||
| 01. The need for model grounding.srt | 5.2 KB | ||
| 02. Similarity search with embeddings.mp4 | 10 MB | ||
| 02. Similarity search with embeddings.srt | 9 KB | ||
| 03. Retrieval-augmented generation.mp4 | 5.4 MB | ||
| 03. Retrieval-augmented generation.srt | 4.1 KB | ||
| 04. Implement a RAG solution.mp4 | 17.2 MB | ||
| 04. Implement a RAG solution.srt | 9.7 KB | ||
| 07. 6. Fine-Tuning LLMs | |||
| 01. Fine-tune models.mp4 | 6.4 MB | ||
| 01. Fine-tune models.srt | 5.6 KB | ||
| 02. Compare fine-tuning with RAG and prompt design.mp4 | 4.2 MB | ||
| 02. Compare fine-tuning with RAG and prompt design.srt | 3.2 KB | ||
| 03. Types of fine-tuning.mp4 | 9.9 MB | ||
| 03. Types of fine-tuning.srt | 9.3 KB | ||
| 04. Create and configure a fine-tuning job.mp4 | 13.9 MB | ||
| 04. Create and configure a fine-tuning job.srt | 6.3 KB | ||
| 05. Deploy and test a fine-tuned model.mp4 | 9.9 MB | ||
| 05. Deploy and test a fine-tuned model.srt | 6.3 KB | ||
| 06. Evaluate models using metrics.mp4 | 9.8 MB | ||
| 06. Evaluate models using metrics.srt | 9.7 KB | ||
| 08. 7. Deploying and Running LLMs in Production | |||
| 01. Overview of model deployment strategies.mp4 | 7.4 MB | ||
| 01. Overview of model deployment strategies.srt | 6.9 KB | ||
| 02. Managed APIs vs. self-hosted models.mp4 | 6.8 MB | ||
| 02. Managed APIs vs. self-hosted models.srt | 5.6 KB | ||
| 03. Trade-offs in local deployment.mp4 | 5.4 MB | ||
| 03. Trade-offs in local deployment.srt | 5.1 KB | ||
| 04. Optimize model size.mp4 | 9.4 MB | ||
| 04. Optimize model size.srt | 8.5 KB | ||
| 09. Conclusion | |||
| 01. Summary and next steps.mp4 | 2.2 MB | ||
| 01. Summary and next steps.srt | 2 KB | ||
| 03. Choosing the right model for your use case.mp4 | 8.9 MB | ||
| 03. Choosing the right model for your use case.srt | 8.3 KB | ||
| 04. Lifecycle of an LLM application.mp4 | 5.1 MB | ||
| 04. Lifecycle of an LLM application.srt | 5.1 KB | ||
| 02. Prerequisites and course outline.mp4 | 2.2 MB | ||
| 02. Prerequisites and course outline.srt | 1.8 KB |
Generative AI and LLMOps: Building, Fine-Tuning, and Deploying LLMs
https://WebToolTip.com
Released 10/2026
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 2h 29m | Size: 259.29 MB
Learn how to efficiently build, fine-tune, and deploy LLMs using a variety of methods, including retrieval-augmented generation and model compression.
Course details
Explore methods to optimize and deploy large language models (LLMs) for a wide range of applications. This course covers the selection of appropriate models, how LLMs process text and generate responses, and how to adapt models to specific applications through techniques such as prompt design, retrieval-augmented generation, and fine-tuning. Learn how to effectively manage model lifecycle from deployment in managed APIs to self-hosting or running on local devices. Discover how to leverage model compression techniques to optimize size and performance for local and edge deployment. Find out how to evaluate models using traditional metrics and advanced techniques like LLM judges. Develop strategies for robust and secure deployments while considering privacy and data governance. Transform AI capabilities in your work environment and beyond. whether you are a data scientist, AI engineer, or tech enthusiast, this course is designed to equip you to be able to deploy efficient AI systems. Note: This course was created by Janani Ravi. We are pleased to host this content in our library.
Skills covered
Large Language Model Operations (LLMOps), Generative AI, Kubernetes, Fine Tuning
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| 2.6 GB | freecoursewb | 1 week | 27 | 28 | |
| 850 MB | freecoursewb | 1 week | 111 | 25 | |
| 1.6 GB | freecoursewb | 1 week | 1 | 7 | |
| 1.6 GB | freecoursewb | 1 week | 0 | 0 | |
| 1.82 GB | unity321d | 1 week | 1 | 1 |
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