AIOps / MLOps / Infrastructure and software engineering for ML

[monperrus]

• https://en.wikipedia.org/wiki/MLOps

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https://github.com/machine-learning-apps/actions-ml-cicd A Collection of GitHub Actions That Facilitate MLOps

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Machine learning operations with GitHub Actions and Kubernetes - GitHub Universe 2019 https://www.youtube.com/watch?v=Ll50l3fsoYs

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TinyMLOps: Operational Challenges for Widespread Edge AI Adoption https://arxiv.org/abs/2203.10923

[mrbgco]

Azure MLOps.

AWS MLOps.

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Apache Beam is an open source unified programming model to define and execute data processing pipelines, including ETL, batch and stream processing https://beam.apache.org/

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The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. https://www.kubeflow.org/

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Tensorboard A suite of visualization tools to understand, debug, and optimize TensorFlow programs for ML experimentation https://www.tensorflow.org/tensorboard

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"In the coming decade, all software development will be assisted by AI. Either the code is going to be generated with the help of AI, or it is going to be reviewed by AI, tested by AI, or even deployed by AI." https://www.tabnine.com/blog/from-ci-to-ai-the-ai-layer-in-your-organization/ https://youtu.be/6YQX0LGaNy8

[monperrus]

Training and Serving Machine Learning Models at Scale. (arXiv:2211.05516v1 [cs.SE])

[bbaudry]

Quality Assurance in MLOps Setting: An Industrial Perspective. http://arxiv.org/abs/2211.12706

[bbaudry]

Edge Impulse: An MLOps Platform for Tiny Machine Learning http://arxiv.org/abs/2212.03332

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Edge Impulse: An MLOps Platform for Tiny Machine Learning. http://arxiv.org/pdf/2212.03332

[bbaudry]

A Data Source Dependency Analysis Framework for Large Scale Data Science Projects. http://arxiv.org/abs/2212.07951

[monperrus]

A fault injection platform for learning AIOps models.

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Studying the Characteristics of AIOps Projects on GitHub

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Building Machine Learning Models Like Open Source Software CACM

[bbaudry]

The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice.

http://arxiv.org/abs/2301.09001

[monperrus]

Scalable End-to-End ML Platforms: from AutoML to Self-serve.

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Towards a change taxonomy for machine learning pipelines EMSE 2023

[bbaudry]

Scaling MLOps education https://github.com/readme/guides/mlops-education

[bbaudry]

Open Source Feature Store for Production ML https://feast.dev/

[monperrus]

seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models https://github.com/SeldonIO/seldon-core

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MLflow and Azure Machine Learning https://learn.microsoft.com/en-us/azure/machine-learning/concept-mlflow

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Semgrep rules for ML

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MLOps in google cloud with Vertex AI: Orchestrate machine learning (ML) workflows using Vertex AI Pipelines.

https://cloud.google.com/vertex-ai/docs/pipelines

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Learning Representations on Logs for AIOps. (arXiv:2308.11526v1 [cs.CL])

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LLMOps: Research and technology for building AI products w/ foundation models. General technology for enabling AI capabilities w/ (M)LLMs: MiniLLM (LLM Distillation), LLM Accelerator, Structured Prompting, Extensible Prompts, and Promptist. Effective and efficient approaches to deploying large AI models in practice: MiniLM(-2), xTune, EdgeFormer, and Aggressive Decoding

https://thegenerality.com/agi/about.html

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Kserve Standardized Serverless ML Inference Platform on Kubernetes https://github.com/kserve/kserve

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Neptune: Track, compare, and share your models in one place https://neptune.ai/

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DVC: ML Experiments Management with Git

• https://dvc.org/
• https://github.com/iterative/dvc