zenml

ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.

LLM Evaluation and Observability System for Football Content

A template for a starter project for ZenML

An end-to-end MLOps project covering everything from data ingestion to deployment using tools like ZenML, MLflow, streamlite and various MLOps libraries

An example MLOps overview of ZenML pipelines from a Huggingface Model Repository to a deployed AWS Sagemaker Endpoint.

Unified Data Pipeline Framework (ZenML + NeMo Curator + DataJuicer +100 Processors) for multi-domain processing

End-to-end pipeline that identifies specialized research papers through automated classification, demonstrated with an LLMOps use case that includes data ingestion, model training, evaluation, and deployment.

A full MLOps workflow for predicting traffic volume using an XGBoost regression model. Built with ZenML, the pipeline includes steps for data ingestion, preprocessing, training, evaluation, and deployment using MLflow.

A pipeline built on ZenML for training digit MNIST dataset using Pytorch, experiment tracking using MLFlow and model deployment using BentoML

Production Level MLOps Project for Titanic Dataset

A project to explore and learn about ZenML and mlflow using the hymenoptera dataset from PyTorch

Предсказание цен на жильё на датасете Ames Housing с использованием ZenML, MLflow и scikit-learn

Testing ZenML

End-to-end MLOps pipeline for breast cancer classification: ZenML, MLflow, DVC, Optuna, FastAPI, Docker, CI/CD and blue-green deployment.

A ZenML-based RAG system for document Q&A with multi-format support. My exploration project to understand how RAG systems work under the hood.

Agent skills for designing and building durable AI agent workflows with Kitaru

This project uses the Brazilian E-Commerce Public Dataset by Olist to analyze customer behavior and sales trends. Utilizing ZenML, I implemented an MLOps workflow for data preprocessing, model training, and deployment, focusing on scalability and performance monitoring.

A complete Agentic RAG infrastructure using ZenML pipelines including ETL, Dataset generation, Embedding, Inference and Evaluation

Machine Learning Operation pipelines for train and monitoring Customer satisfaction model. Created using ZenML framework.

End-to-end predictive marketing analytics for refrigerated tablespreads using 1.3M IRI POS rows (2018–2022). Reproducible pipeline with DVC data, MLflow tracking & registry, temporal validation (holdout R² = 0.635), a served FastAPI model and a Streamlit business dashboard.