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Back to googlecloudplatform/mlops-on-gcp

Open-source alternatives to Mlops On Gcp

15 open-source projects similar to googlecloudplatform/mlops-on-gcp, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Mlops On Gcp alternative.

  • alirezadir/production-level-deep-learningalirezadir avatar

    alirezadir/Production-Level-Deep-Learning

    4,647View on GitHub↗

    This project is an MLOps architectural guide and framework for designing and deploying deep learning systems into production environments. It provides a structured approach to model inference deployment, ML pipeline orchestration, and the creation of production-level machine learning architectures. The project distinguishes itself through a focus on distributed deep learning and edge AI optimization. It covers methodologies for parallelizing model training across multiple GPUs to handle large datasets and applies techniques like quantization and distillation to reduce model size for embedded

    aiartificial-intelligencedeep-learning
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  • aporia-ai/mlplatform-workshopA

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  • aronchick/mlops-pipelineA

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  • axsaucedo/seldon-coreA

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  • bartgras/4ab9c716167b5d9aee6a222f7301ac60B

    bartgras/4ab9c716167b5d9aee6a222f7301ac60

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  • ckaestne/seaiC

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  • cmawer/reproducible-modelC

    cmawer/reproducible-model

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  • dslp/dslp-repo-templatedslp avatar

    dslp/dslp-repo-template

    202View on GitHub↗

    Template repository for data science lifecycle project

    Python
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  • graviraja/mlops-basicsgraviraja avatar

    graviraja/MLOps-Basics

    8,585View on GitHub↗

    MLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline. The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardiz

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    View on GitHub↗8,585
  • se-ml/awesome-semlSE-ML avatar

    SE-ML/awesome-seml

    1,358View on GitHub↗

    A curated list of articles that cover the software engineering best practices for building machine learning applications.

    awesomeawesome-listdeep-learning
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  • visenger/handson-mlV

    visenger/handson-ml

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  • visenger/ml-project-templateV

    visenger/ml-project-template

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  • visenger/mlopsV

    visenger/MLOps

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  • visenger/mlspecV

    visenger/MLSpec

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