Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
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Updated
Sep 30, 2025 - Jupyter Notebook
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Notebooks and Code about Generative Ai, LLMs, MLOPS, NLP , CV and Graph databases
A notebook showing how to easily convert a current notebook you have to a notebook that can be run on Kubeflow Pipelines.
📚 Jupyter Notebooks extension for versioning, managing and sharing notebook checkpoints in your machine learning and data science projects.
Render Jupyter Notebooks With Metaflow Cards
Convert monolithic Jupyter notebooks 📙 into maintainable Ploomber pipelines. 📊
cube studio开源云原生一站式机器学习/深度学习/大模型AI平台,mlops算法链路全流程,算力租赁平台,notebook在线开发,拖拉拽任务流pipeline编排,多机多卡分布式训练,超参搜索,推理服务VGPU虚拟化,边缘计算,标注平台自动化标注,deepseek等大模型sft微调/奖励模型/强化学习训练,vllm/ollama/mindie大模型多机推理,私有知识库,AI模型市场,支持国产cpu/gpu/npu 昇腾生态,支持RDMA,支持pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/ray/volcano等分布式
Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
Fast and easy Jupyter notebooks
Slides and notebook for the workshop on serving bert models in production
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
The Machine Learning project including ML/DL projects, notebooks, cheat codes of ML/DL, useful information on AI/AGI and codes or snippets/scripts/tasks with tips.
Example notebooks using the TurboML platform
Transform messy data science notebooks into production-ready code. Examples covering testing, CI/CD, MLOps, and scalable deployment practices.
SageMakerで機械学習モデルを構築、学習、デプロイする方法が学べるNotebookと教材集
Python Machine Learning (ML) project that demonstrates the archetypal ML workflow within a Jupyter notebook, with automated model deployment as a RESTful service on Kubernetes.
Pediatric Bone Age Assessment
A Jupyter Notebook collection designed to develop a practical understanding of Machine Learning Operations (MLOps) defined in the NESA Software Engineering Course Specifications pg 27.
Machine learning operator & controller for Kubernetes
"1 config, 1 command from Jupyter Notebook to serve Millions of users", Full-stack On-Premises MLOps system for Computer Vision from Data versioning to Model monitoring and drift detection.
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