Mlops
Showing 33 of 69 projects
A library for evaluating TensorFlow models on large datasets with distributed computation and slicing analysis.
A Python toolbox for explainable AI, providing tools for data analysis, model evaluation, and bias mitigation in machine learning.
A hands-on tutorial for training and deploying a machine learning model as a serverless REST API to predict cryptocurrency prices.
A scalable library for exploring, validating, and monitoring machine learning data, integrated with TensorFlow and TFX.
An open-source toolkit for auditing bias and experimenting with fairness methods in machine learning models.
A tool to package, serve, and deploy any ML model on any platform using a GitOps approach.
IPython-based environment for reproducible machine learning research with unified wrappers for multiple ML libraries.
Capture, analyze, and transform messy Jupyter notebooks into production data pipelines with just two lines of code.
Open-source teaching materials for a practical Machine Learning in Finance course, focusing on industry tools and real-world use cases.
An engine for ML/data tracking, visualization, explainability, drift detection, and dashboards, integrated with Polyaxon.
An open-source MLOps framework for defining and deploying machine learning and LLM workloads across any cloud infrastructure.
A book teaching practical patterns for building scalable and reliable distributed machine learning systems using Kubernetes, TensorFlow, Kubeflow, and Argo Workflows.
A toolkit that streamlines and automates the generation of model cards for machine learning models.
A collection of CI pipelines, Docker images, and optimized examples to simplify JAX development on NVIDIA GPUs.
An open-source Python library for detecting concept and data drift in machine learning systems.
A Python library for monitoring model and data drift over time, generating insightful HTML reports for AI governance.
A Python library for logging ML metrics, parameters, and models in simple file formats, compatible with DVC and Git.
A collection of examples demonstrating how to use Comet.ml for machine learning experiment tracking across various Python frameworks.
Kubernetes operator for self-hosted LLM inference across a heterogeneous GPU fleet: NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. Runtimes: llama.cpp, vLLM, TGI, mlx-server. Multi-GPU sharding, model caching, OpenAI-compatible endpoints. Apache-2.0, run across homelab and on-prem fleets, actively developed.
A modern, open-source, on-premise CI/CD system focused on testing and scalability.
A GitHub Action to build and push Jupyter-enabled Docker images from data science repositories using repo2docker.
A GitHub template for automating machine learning workflows on Azure using GitHub Actions.
A PMML evaluator library for Apache Spark that provides ML-compatible transformers for deploying predictive models.
A package manager for machine learning datasets and models with a CLI and self-hostable registry.
Deploy and version machine learning models in Ruby applications using object storage like Amazon S3.
A GitHub Action that enables ChatOps by triggering workflows from PR comments using custom commands.
A GitHub Action that retrieves model runs and metrics from Weights & Biases for integration into CI/CD workflows.
A GitHub Action that submits Argo Workflows from GitHub Actions to enable CI/CD for machine learning on Kubernetes.
A GitHub Action for compiling and deploying Kubeflow Pipelines to a Kubeflow cluster on Google Cloud Platform.
A GitHub Action that submits Argo workflows to Google Kubernetes Engine for automated machine learning testing and CI/CD.
GitHub Action that fetches kubeconfig from Google Kubernetes Engine (GKE) for use in CI/CD workflows.
A VS Code extension for viewing large datasets (JSONL/Parquet/CSV) instantly without crashes, with 16 production LLM tokenizers for accurate token counting.
A Python library for comprehensive machine learning model evaluation with plots, reports, experiment tracking, and notebook analysis.
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