MLOps: Building a CI/CD Pipeline for ML Models on Azure Databricks
9 min read4.7k
Most ML teams are great at training models. Very few are great at shipping them. The gap between a notebook that works and a model that reliably serves producti...
#MLSystems
6 posts
Building a Vector Index in Azure AI Search: HNSW, Profiles, and RAG Retrieval
13 min read5.6k
In this article, we will understand how vector search works in Azure AI Search and how to use it as the retrieval layer in a Retrieval-Augmented Generation (RAG...
Amazon Q Developer for AI Infrastructure: Architecting Automated ML Pipelines
6 min read5.8k
Introduction The landscape of Machine Learning Operations (MLOps) is shifting from manual configuration to AI-driven orchestration. As organizations scale their...
Real-Time Feature Store (Online + Offline Serving)
7 min read4.8k
In the modern ML landscape, the bottleneck for productionizing models has shifted from model architecture to data engineering. Companies like Uber, Netflix, and DoorDash have pioneered the concept of ...
Feature Store for Real-Time ML Inference (Meta)
7 min read6.6k
In the modern ML lifecycle, the bottleneck has shifted from model architecture to data engineering. At organizations like Meta, Uber, and Netflix, the challenge isn't just training a model with billio...
Designing an ML Pipeline for Production (Feature Store + Training + Serving)
6 min read5.1k
In the evolution of a technology company, there is a distinct "Maturity Gap" between a data scientist training a model in a Jupyter notebook and a software engineer deploying a high-availability distr...