Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
-
Updated
Jul 25, 2024 - Jupyter Notebook
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
A notebook showing how to easily convert a current notebook you have to a notebook that can be run on Kubeflow Pipelines.
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.
📚 Jupyter Notebooks extension for versioning, managing and sharing notebook checkpoints in your machine learning and data science projects.
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,支持sso登录,多租户,大数据平台对接,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,边缘计算,serverless,标注平台,自动化标注,数据集管理,大模型微调,vllm大模型推理,llmops,私有知识库,AI模型应用商店,支持模型一键开发/推理/微调,支持国产cpu/gpu/npu芯片,支持RDMA,支持pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/spark/ray/volcano分布式
An extensive library of AI resources including books, courses, papers, guides, articles, tutorials, notebooks, AI field advancements and more.
Render Jupyter Notebooks With Metaflow Cards
Fast and easy Jupyter notebooks
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Slides and notebook for the workshop on serving bert models in production
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.
SageMakerで機械学習モデルを構築、学習、デプロイする��法が学べるNotebookと教材集
Pediatric Bone Age Assessment
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.
⚡️SwanLab: your ML experiment notebook. 你的AI实验笔记本,跟踪与可视化你的机器学习全流程
Convert Python scripts and notebooks to reproducible production code and run it on cloud GPUs
A compute framework for turning multimodal data structures into vector embeddings, to improve quality and control when working with LLMs. Generate custom multimodal embeddings with ease and weigh the vector parts separately at query time, removing the need for custom re-ranking models. Deploy straight from notebook to production.
Machine Learning Operator & Controller for Kubernetes
Machine Learning Engineering Notebook
Add a description, image, and links to the mlops topic page so that developers can more easily learn about it.
To associate your repository with the mlops topic, visit your repo's landing page and select "manage topics."