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LLM Engineer's Handbook
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Engineer's framework for LLM & RAG evaluation
A hands-on guide to measuring what matters
22 mins ago
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Paul Iusztin
5
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Engineer's framework for LLM & RAG evaluation
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The ultimate LLM fine-tuning pipeline
Master production-ready fine-tuning with AWS SageMaker, Unsloth, and MLOps best practices
Nov 16
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Paul Iusztin
25
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The ultimate LLM fine-tuning pipeline
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Connecting the dots in data and AI systems
Simplifying MLE & MLOps with the FTI Architecture
Nov 9
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Paul Iusztin
10
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Connecting the dots in data and AI systems
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4
ML serving 101: Core architectures
Choose the right architecture for your AI/ML app
Nov 2
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Paul Iusztin
25
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ML serving 101: Core architectures
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October 2024
The ABCs of Diffusion Models
Your Guide to ML’s Next Big Thing
Oct 26
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Anca Ioana Muscalagiu
15
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The ABCs of Diffusion Models
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LLM Engineer's Handbook is finally live!
A framework for building LLM and RAG apps
Oct 22
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Paul Iusztin
36
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LLM Engineer's Handbook is finally live!
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14
Inside vector DBs: A simple guide
Understand the fundamentals of vector databases
Oct 19
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Paul Iusztin
18
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Inside vector DBs: A simple guide
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4
Instagram data mining using LLMs
Crawl Instagram posts and use LLMs to extract critical insights for business growth.
Oct 12
•
Vlad Adumitracesei
16
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Instagram data mining using LLMs
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6
Custom AI solutions are overrated
Build a production RAG application in 30 minutes using Snowflake
Oct 5
•
Paul Iusztin
14
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Custom AI solutions are overrated
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September 2024
The 6 MLOps foundational principles
The core MLOps guidelines for production ML
Sep 28
•
Paul Iusztin
27
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The 6 MLOps foundational principles
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2
Build a semantic news search engine with 0 delay
How to build a real-time news search engine using Kafka, vector DBs, RAG and streaming engines.
Sep 21
•
Paul Iusztin
28
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Build a semantic news search engine with 0 delay
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Embeddings: the cornerstone of AI & ML
Fundamentals of embeddings: what they are, how they work, why they are so powerful and how they are created.
Sep 14
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Paul Iusztin
24
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Embeddings: the cornerstone of AI & ML
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