Paul Iusztin

Paul Iusztin

Featured Book

LLM Engineer's Handbook: Master the art of engineering large language models from concept to production

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

2024

Maxime Labonne, et al.

Maxime Labonne, Paul Iusztin

Stories

Pinned

Published in
Decoding ML

An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin

From data gathering to productionizing LLMs using LLMOps good practices.

Mar 16, 2024

2.1K

An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin

Mar 16, 2024

2.1K

Pinned

Published in
Decoding ML

The LLMs kit: Build a production-ready real-time financial advisor system using streaming…

Lesson 1: LLM architecture system design using the 3-pipeline pattern

Jan 5, 2024

591

The LLMs kit: Build a production-ready real-time financial advisor system using streaming…

Jan 5, 2024

591

Join me for content on building production-grade ML/AI systems

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Pinned

Published in
TDS Archive

A Framework for Building a Production-Ready Feature Engineering Pipeline

Lesson 1: Batch Serving. Feature Stores. Feature Engineering Pipelines.

Apr 28, 2023

937

A Framework for Building a Production-Ready Feature Engineering Pipeline

Apr 28, 2023

937

Published in
Decoding ML

The Ultimate Prompt Monitoring Pipeline

Master monitoring complex traces and evaluation while in production

Nov 30, 2024

102

Nov 30, 2024

102

Published in
Decoding ML

Beyond Proof of Concept: Building RAG Systems That Scale

A hands-on guide to architecting production LLM inference pipelines with AWS SageMaker

Nov 18, 2024

125

Beyond Proof of Concept: Building RAG Systems That Scale

Nov 18, 2024

125

Published in
Decoding ML

The Engineer’s Framework for LLM & RAG Evaluation

Stop guessing if your LLM works: A hands-on guide to measuring what matters

Nov 18, 2024

100

The Engineer’s Framework for LLM & RAG Evaluation

Nov 18, 2024

100

Published in
Decoding ML

8B Parameters, 1 GPU, No Problems: The Ultimate LLM Fine-tuning Pipeline

Master production-ready fine-tuning with AWS SageMaker, Unsloth, and MLOps best practices

Nov 18, 2024

110

8B Parameters, 1 GPU, No Problems: The Ultimate LLM Fine-tuning Pipeline

Nov 18, 2024

110

Published in
Decoding ML

Turning Raw Data Into Fine-Tuning Datasets

How to automatically generate instruction datasets for fine-tuning LLMs on custom data

Nov 18, 2024

161

Turning Raw Data Into Fine-Tuning Datasets

Nov 18, 2024

161

Published in
Decoding ML

I Replaced 1000 Lines of Polling Code with 50 Lines of CDC Magic

The MongoDB + RabbitMQ stack that's revolutionizing LLM data pipelines

Nov 18, 2024

107

I Replaced 1000 Lines of Polling Code with 50 Lines of CDC Magic

Nov 18, 2024

107

Published in
Decoding ML

Your Content is Gold: I Turned 3 Years of Blog Posts into an LLM Training

A practical guide to building custom instruction datasets for fine-tuning LLMs

Nov 18, 2024

Your Content is Gold: I Turned 3 Years of Blog Posts into an LLM Training

Nov 18, 2024

Featured Book

Stories

An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin

From data gathering to productionizing LLMs using LLMOps good practices.

The LLMs kit: Build a production-ready real-time financial advisor system using streaming…

Lesson 1: LLM architecture system design using the 3-pipeline pattern

Join me for content on building production-grade ML/AI systems

A Framework for Building a Production-Ready Feature Engineering Pipeline

Lesson 1: Batch Serving. Feature Stores. Feature Engineering Pipelines.

The Ultimate Prompt Monitoring Pipeline

Master monitoring complex traces and evaluation while in production

Beyond Proof of Concept: Building RAG Systems That Scale

A hands-on guide to architecting production LLM inference pipelines with AWS SageMaker

The Engineer’s Framework for LLM & RAG Evaluation

Stop guessing if your LLM works: A hands-on guide to measuring what matters

8B Parameters, 1 GPU, No Problems: The Ultimate LLM Fine-tuning Pipeline

Master production-ready fine-tuning with AWS SageMaker, Unsloth, and MLOps best practices

Turning Raw Data Into Fine-Tuning Datasets

How to automatically generate instruction datasets for fine-tuning LLMs on custom data

I Replaced 1000 Lines of Polling Code with 50 Lines of CDC Magic

The MongoDB + RabbitMQ stack that's revolutionizing LLM data pipelines

Your Content is Gold: I Turned 3 Years of Blog Posts into an LLM Training

A practical guide to building custom instruction datasets for fine-tuning LLMs

📝 NLP

🤖 Generative AI

⭕️ MLOps