3 posts tagged with "platform"

If your platform team’s North Star is getting every development squad into the “elite” performer bracket for DORA metrics, you’re aiming at the wrong target. You’re probably making things worse. I’ve watched organizations obsess over average deployment frequency or lead time, only to see platform complexity balloon and team friction increase. The real goal isn’t to build a rocket ship for your top performers; it’s to build a reliable highway for everyone else.

The corrective lens comes from a pivotal but under-appreciated source: the CNCF’s Platform Engineering Metrics whitepaper. It makes a contrarian, data-backed claim that cuts through the industry hype. The paper states bluntly that platform teams should focus on “improving the performance of the lowest-performing teams” and “reducing the spread of outcomes, not just the average.” This isn’t about settling for mediocrity. It’s about systemic stability and scaling effectively. When you measure platform success by how much you compress the variance in team performance, you start building for adoption and predictability—not vanity metrics.

Introduction​

Qlib is an AI-oriented, open-source platform from Microsoft that simplifies the entire quantitative finance process. By leveraging PyTorch, Qlib can seamlessly integrate modern neural networks—like Multi-Layer Perceptrons (MLPs)—to process large datasets, engineer alpha factors, and run flexible backtests. In this post, we focus on a PyTorch MLP pipeline for Alpha360 data in the US market, examining a single YAML configuration that unifies data ingestion, model training, and performance evaluation.

Introduction​

In the realm of quantitative finance, machine learning and deep learning are revolutionizing how researchers and traders discover alpha, manage portfolios, and adapt to market shifts. Qlib by Microsoft is a powerful open-source framework that merges AI techniques with end-to-end finance workflows.

This article demonstrates how Qlib automates an AI-driven quant workflow—from data ingestion and feature engineering to model training and backtesting—using a single YAML configuration for a LightGBM model. Specifically, we’ll explore the AI-centric aspects of how qrun orchestrates the entire pipeline and highlight best practices for leveraging advanced ML models in your quantitative strategies.