2 Lines of code to track ML experiments + EDA + check into Github
ML Experimentation Platform
Unsorted Playground for Machine Learning, Reinforcement Learning and other AI Experiments
lightweight MCP server for ClearML
A hands-on collection of machine learning concepts, algorithms, and practical implementations. Designed to help learners and practitioners apply ML techniques with clarity and efficiency. 🚀
Contains code and slides for our talk at Cloud Next 2022: Better Hardware Provisioning for ML Experiments on GCP.
A lightweight Python library for reproducible computational experiments with an ultra-simple, smart API. From idea to insight in under 5 minutes, with zero configuration.
An application of the WhizML codebase for an analysis of cardiovascular disease risk.
A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.
An application of the WhizML codebase for an analysis of Walmart weekly sales.
This project demonstrates how to track, manage, and visualize machine learning experiments using MLflow and integrate with DagsHub for version control and collaboration. It covers experiment tracking, model logging, and reproducible experiments in a collaborative environment.
Metrics aggregation and alerting for ML experiments—multi-backend export (Prometheus, InfluxDB, Datadog, OpenTelemetry), advanced aggregations (percentiles, histograms, moving averages), threshold-based alerting with anomaly detection (z-score, IQR), and time-series storage. Research-grade observability for the NSAI ecosystem.
Docker for getting jax to work with cuda, for reproducing ml experiments like eicl. Sure, let's NOT make a compatibility matrix and let people fight for their lives on cuda
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