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Decoding ML
Engineering production ML systems
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LLMs & RAG to Production Course
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Ml System Design
Building ML Systems the Right Way Using the FTI Architecture
Building ML Systems the Right Way Using the FTI Architecture
The fundamentals of the FTI architecture that will help you build modular and scalable ML systems using MLOps best practices.
Paul Iusztin
Aug 9
Architect scalable and cost-effective LLM & RAG inference pipelines
Architect scalable and cost-effective LLM & RAG inference pipelines
Design, build and deploy RAG inference pipeline using LLMOps best practices.
Paul Iusztin
May 31
SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG — in Real-Time!
SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG — in Real-Time!
Use a Python streaming engine to populate a feature store from 4+ data sources
Paul Iusztin
Apr 19
How to build a Real-Time News Search Engine using Serverless Upstash Kafka and Vector DB
How to build a Real-Time News Search Engine using Serverless Upstash Kafka and Vector DB
A hands-on guide to implementing a live news aggregating streaming pipeline with Apache Kafka, Bytewax, and Upstash Vector Database.
Alex Razvant
Apr 12
Mlops
The 6 MLOps foundational principles
The 6 MLOps foundational principles
The core MLOps guidelines for production ML
Paul Iusztin
Sep 21
Experiment Tracking Essentials: Finding the Right Tool
Experiment Tracking Essentials: Finding the Right Tool
Gradio’s Custom Dashboards vs Wandb’s Built-In Tools for Training Diffusion Models
Anca Ioana Muscalagiu
Sep 4
The Role of Feature Stores in Fine-Tuning LLMs
The Role of Feature Stores in Fine-Tuning LLMs
From raw data to instruction dataset
Vesa Alexandru
May 10
Fix your messy ML configs in your Python projects
Fix your messy ML configs in your Python projects
2024 MLOps learning roadmap. Python syntax sugar that will help you write cleaner code.
Paul Iusztin
Mar 22
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