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Achievements

Achievement: Pull Sharkx3Achievement: QuickdrawAchievement: Starstruckx3Achievement: Pair ExtraordinaireAchievement: YOLOAchievement: Arctic Code Vault Contributor

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@decodingml

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iusztinpaul/README.md

Paul Iusztin

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Senior AI/ML Engineer • Founder @ Decoding ML ~ Articles and courses about building production-grade AI systems.


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🎯 What I do

  • Design and implement production-ready AI, GenAI and information retrieval systems
  • Love using MLOps and Software Engineering best practices
  • Teach people about the process through my blog and newsletter

🔗 Socials

🤝 Let's collaborate!

We're always excited to collaborate with AI practitioners and creators. Have an idea or want to work together?

📧 Reach out at p.b.iusztin@gmail.com

Decoding ML Logo

Founder @ Decoding ML

A channel for proven content on designing, coding, and deploying production-grade AI, GenAI, and information retrieval systems - built with ML systems, MLE, and MLOps best practices.

🗞️ Join Decoding ML

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  1. PacktPublishing/LLM-Engineers-Handbook Public

    The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

    Python 3k 615

  2. decodingml/second-brain-ai-assistant-course Public

    Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.

    Jupyter Notebook 590 104

  3. neural-maze/philoagents-course Public

    When Philosophy meets AI Agents

    Python 11 1

  4. decodingml/personalized-recommender-course Public

    👕 Open-source course on architecting, building and deploying a real-time personalized recommender for H&M fashion articles.

    Jupyter Notebook 343 71

  5. decodingml/llm-twin-course Public

    🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

    Python 3.7k 620

  6. decodingml/tabular-semantic-search-tutorial Public

    📚 Tutorial on building a modern search app for Amazon e-commerce products leveraging tabular semantic search and natural language queries.

    Jupyter Notebook 49 11