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...
#MLOps
6 posts
Databricks Workflows: Orchestrating Multi-Task ML Pipelines Without the Airflow Overhead
7 min read5.4k
Airflow is powerful but it comes with a tax: a server to maintain, a DAG repo to manage, a scheduler to babysit, and a support burden every time a new team memb...
Migrating from AzureML SDK v1 to azure-ai-projects in Microsoft Foundry
6 min read4.6k
AzureML SDK v1 reaches end-of-support on June 30, 2026. CLI v1 already hit end-of-support in September 2025. If you have production pipelines built on azureml-s...
Vertex AI Pipelines: Production-Grade ML on GCP
6 min read5.1k
The transition from experimental machine learning (ML) to production-grade systems is often referred to as the "Valley of Death" for data science projects. While training a model in a notebook is stra...
Hosting LLMs on AWS: ECS vs EKS vs SageMaker
6 min read5.9k
The rapid proliferation of Large Language Models (LLMs) like Llama 3, Mistral, and Falcon has shifted the cloud engineering focus from model training to efficient, scalable inference. For organization...
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...