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Decoding ML
Engineering production ML systems
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Ml System Design
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.
Razvant Alexandru
Apr 12
Change Data Capture: Enabling Event-Driven Architectures
Change Data Capture: Enabling Event-Driven Architectures
Transforming Data Streams: The Core of Event-Driven Architectures
Vesa Alexandru
Apr 5
Mlops
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
Master ML configuration files using OmegaConf and Hydra
Master ML configuration files using OmegaConf and Hydra
How to handle complex configurations without changing code.
Razvant Alexandru
Apr 18
How to ensure your deep learning stack is fail-safe in production?
How to ensure your deep learning stack is fail-safe in production?
Build an end-to-end monitoring dashboard using Prometheus, Triton, and Grafana.
Razvant Alexandru
Apr 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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