iusztinpaul/hands-on-llms

• highreadme#1

  Reposition README's opening to clearly state the course's identity and then its status

  Why:

  CURRENT

  ## 🚨 Remastered Course 🚨 As the world of GenAI and LLMs moves fast, too fast for educational content, it was easier to archive this course and create a new one from scratch. Check out our new LLM Twin open-source course for an improved experience in learning to build a production-ready LLM and RAG system. ## 🚨 Remastered Course 🚨 <div align="center"> <h2>Hands-on LLMs Course </h2> <h1>Learn to Train and Deploy a Real-Time Financial Advisor</h1> <i>by <a href="https://github.com/iusztinpaul">Paul Iusztin</a>, <a href="https://github.com/Paulescu">Pau Labarta Bajo</a> and <a href="https://github.com/Joywalker">Alexandru Razvant</a></i> </div>

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  ## Hands-on LLMs Course: Learn to Train and Deploy a Real-Time Financial Advisor
  This repository provides a comprehensive, free course on LLMs, LLMOps, and vector databases, guiding you through designing, training, and deploying a real-time financial advisor LLM system. Please note: As the world of GenAI and LLMs moves fast, this course has been archived and a new one created. For an updated experience, check out our new LLM Twin open-source course.

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• mediumreadme#2

  Add a 'What this course is (and isn't)' section to the README

  Why:

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  ## What this course is (and isn't)
  This repository provides a hands-on course for learning to build and deploy LLM systems, focusing on practical implementation. It is *not* a production-ready framework, an LLM model, or a data streaming platform. Instead, it teaches you how to *use* and *integrate* tools like LangChain, Qdrant, and streaming technologies to create your own LLM applications.

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• lowtopics#3

  Add topics to explicitly categorize the repo as a learning resource

  Why:

  CURRENT

  3-pipeline-design, aws, beam, bytewax, cicd, comet-ml, docker, fine-tuning, generative-ai, huggingface, langchain, llmops, llms, mlops, qdrant, qlora, streaming, transformers

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  3-pipeline-design, aws, beam, bytewax, cicd, comet-ml, docker, fine-tuning, generative-ai, huggingface, langchain, llmops, llms, mlops, qdrant, qlora, streaming, transformers, llm-course, generative-ai-tutorial, hands-on-learning

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to build and deploy a real-time financial advisory LLM system?

  you: not recommended
  AI recommended (in order):

  1. OpenAI GPT-4
  2. GPT-3.5 Turbo
  3. Anthropic Claude 3
  4. Opus
  5. Sonnet
  6. Google Gemini
  7. Gemini Advanced
  8. Gemini Pro
  9. Meta Llama 3
  10. Mistral Large
  11. Mixtral 8x7B
  12. Apache Kafka
  13. Apache Flink
  14. Confluent Platform
  15. Pinecone
  16. Weaviate (weaviate/weaviate)
  17. Chroma (chroma-core/chroma)
  18. LangChain (langchain-ai/langchain)
  19. LlamaIndex (run-llama/llama_index)
  20. Microsoft Semantic Kernel (microsoft/semantic-kernel)
  21. Kubernetes
  22. Google Kubernetes Engine
  23. Amazon EKS
  24. Azure Kubernetes Service
  25. AWS SageMaker
  26. Google Cloud Vertex AI
  27. Azure Machine Learning
  28. FastAPI (tiangolo/fastapi)
  29. Prometheus (prometheus/prometheus)
  30. Grafana (grafana/grafana)
  31. OpenTelemetry
  32. HashiCorp Vault (hashicorp/vault)

  AI recommended 32 alternatives but never named iusztinpaul/hands-on-llms. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  What are the best practices for LLM MLOps with streaming data pipelines?

  you: not recommended
  AI recommended (in order):

  1. Apache Kafka
  2. Apache Flink
  3. Databricks Delta Live Tables (DLT)
  4. Feast
  5. Tecton
  6. Hugging Face Transformers
  7. PyTorch Lightning
  8. Ray Train
  9. NVIDIA Triton Inference Server
  10. KServe
  11. OpenAI API
  12. Azure OpenAI Service
  13. Google Cloud Vertex AI
  14. MLflow
  15. Prometheus
  16. Grafana
  17. Arize AI
  18. WhyLabs

  AI recommended 18 alternatives but never named iusztinpaul/hands-on-llms. This is the gap to close.

  Show full AI answer

• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of iusztinpaul/hands-on-llms?

  pass
  ✓AI named iusztinpaul/hands-on-llms explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts iusztinpaul/hands-on-llms in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named iusztinpaul/hands-on-llms explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo iusztinpaul/hands-on-llms solve, and who is the primary audience?

  pass
  ⚠AI did not name iusztinpaul/hands-on-llms — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?