model-development

MARVIS-Agent: all-purpose credit risk agent for model development, validation, data processing, feature engineering, and strategy workflows.

⚠️ [Deprecated] SDK for the Geti™ platform (v2.x only)

Develop a deep learning model capable of predicting traffic flow in urban environments. The model will utilize historical traffic data, weather conditions, and road configurations to forecast traffic patterns. This information can be invaluable for traffic management systems, helping to optimize traffic signals and reduce congestion, ultimately.

A verification pipeline for evaluating models and forecasts.

The system will analyse and predict patterns in electricity demand, renewable power generation, and battery storage, enabling automated, intelligent decisions that minimize energy waste and reduce costs.

End-to-End MLOps pipeline on Google Cloud with Vertex AI, Cloud Build, Cloud Deploy, Docker, and Artifact Registry for automated model training and deployment.

🔬 Flask API for the Neuro Gambling Scanner project – Predicts gambling addiction risk using machine learning and returns personalized interventions.

This repository contains a collection of AI and machine learning projects built to explore different models, techniques, and real-world problem-solving approaches. Each project demonstrates practical implementation and serves as a reference for learning, experimentation, and further development in AI.

A Cloud Computing Project Template

Flask for training/testing Watson, FastText, Gensen Embeddings and hDBScan. Plus it supports MLFlow for model info logging.

Manage model artifacts throughout the model development process.

Learn with me as we build a llama-like model for Tenstorrent hardware

Verifiable Labs: a commercial AI model company researching increasingly general, self-improving intelligence.

User-directed Web interface and research harness for reproducible method development with Hermes agents.

End-to-End ML Pipeline is an MLOps project demonstrating reproducible machine-learning workflows using DVC for data/model versioning and AWS S3 for remote storage, with automated pipeline stages and experiment tracking.

Research scope, evidence, architecture, and contributor entrypoints for the Verifiable Labs model research program.

This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

A streamlined app for traditional ML model training and deployment. Features include interactive data visualization, model optimization tools, and automated deployment capabilities. Ideal for data scientists and ML engineers looking to simplify their workflow.