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Machine Learning Operations - An awesome list of references for MLOps.
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Hydrosphere is a GitHub repository for an MLOps platform. The repository provides documentation for getting started with the platform, including installation instructions and guidance for deployment using Docker and Kubernetes.
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Open source MLOps project that eases model handoffs between data scientist and DevOps.
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Neuraxle is an AutoML library that supports hyperparameter tuning via pipeline abstractions to build clean deep learning production pipelines. Pipeline steps can have associated hyperparameter spaces, and the library is compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks, and MLOps environments.
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MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.
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An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.
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19 Questions is an open source machine learning and Bayesian inference engine designed to assign attributes to objects. Its source code is hosted in a public GitHub repository under the fulldecent organization, and contributions to the project are accepted from the public.
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agenttrace is a local-first terminal TUI and report generator for AI coding-agent session history. It reads logs from several coding agents and produces reports on cost, tokens, time, and slow-run diagnosis. Prompts, code, and logs stay on the local machine.
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A deprecated GitHub repository described as an AI Gateway, a core infrastructure stack for building production-ready AI applications. It provides a universal API for inferencing over 100 LLMs and lists supported providers and endpoints such as OpenAI and Groq.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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ANEE is an experimental dynamic inference wrapper for pretrained Transformer language models, currently GPT-2. It exposes an energy_budget and performs early exit inside the model's forward pass rather than always running all layers. It adds per-token adaptive computation using a profiler and learned controller to skip redundant layers while keeping the KV-cache aligned.
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Applied ML is a curated collection of papers, articles and blogs about data science and machine learning in production, organized under topics such as data quality, recommendation, search and ranking, and MLOps. It is licensed under MIT and covers how organizations frame problems and which techniques worked.
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Drop-in AsyncOpenAI replacement that transparently batches requests via the Batch API for cheaper LLM inference.
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Awesome AutoDL is a curated list of automated deep learning resources and an in-depth analysis, subtitled that neural architecture search is not the end. It collects papers, libraries and benchmarks covering neural architecture search and hyper-parameter optimization.
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BayesPy is an open-source Python library that provides tools for performing Bayesian inference. It is hosted as a public repository on GitHub where users can access the code and contribute to its development.
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BentoML is a unified model serving framework. It enables users to build model inference APIs, job queues, LLM applications, and multi-model pipelines. The framework supports local execution and deployment via Docker.
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An open-source LLM gateway with routing, load balancing, guardrails, and observability for 1000+ models. opensource.
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bitnet.cpp is the official inference framework for 1-bit LLMs such as BitNet b1.58. It provides optimized kernels for inference of 1.58-bit models on CPU and GPU, with NPU support stated as coming next. The repository includes documentation and setup scripts, and the project is released under the MIT license.
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BudgetML is a tool for deploying a machine learning inference service to a secured HTTPS API endpoint with less than 10 lines of code. It runs the model on a Google Cloud Platform preemptible instance, generates a FastAPI server endpoint, and provides interactive Swagger docs. The project states that the library is not being actively maintained.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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ByteHub is an open-source tool designed to simplify the creation and management of feature stores. It is maintained as a public repository on GitHub under the bytehub-ai organization, with documentation and tutorials available for users.
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Open-source project that supports AI agents, automated workflows, orchestration, or delegated tasks.
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Clip-as-a-service is an open-source tool that provides scalable embedding, reasoning, and ranking capabilities for images and text using the CLIP model. It is maintained as a public repository on GitHub under the jina-ai organization, with documentation and installation guides available.
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CML, or Continuous Machine Learning, is an open-source CLI tool for implementing CI/CD with a focus on MLOps. It is used to automate development workflows, including machine provisioning, model training and evaluation, comparing ML experiments across project history, and monitoring changing datasets. It can generate a visual report with results and metrics automatically on every pull request, using GitLab, GitHub, or Bitbucket.