This is a learning project towards E2E data science.
A simpler and more pragmatic definition is that an end-to-end data scientist can identify the problem, design a solution, ship it, and measure outcomes. -- Eugene Yan
I hope to learn and share detailed step-by-step guides on how solve ML (for now deep learning & computer vision) problems -- identification, design, deployment, and monitoring.
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MLOps
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SIG MLOps defines “an optimal MLOps experience [as] one where Machine Learning assets are treated consistently with all other software assets within a CI/CD environment. Machine Learning models can be deployed alongside the services that wrap them and the services that consume them as part of a unified release process.” By codifying these practices, we hope to accelerate the adoption of ML/AI in software systems and fast delivery of intelligent software.
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Design
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Machine Learning Systems Design
Machine learning systems design is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified requirements.
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Training
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This book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code.
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Deployment
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Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying AI systems in the real world.
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Somethings shouldn't be here, but as I write these words I think of them. Sometime I will reorganize this better -- maybe a new repo called workflow.
- vim/neovim/jupyter/etc. TODO: create repo.
- python: code quality / best practices in documentation, testing and style. TODO: create repo.
- packages: pytorch, keras, guildai, etc.
- linux/arch linux: optimize workflow. TODO: create repo.
- organization: roam research, quire etc.
- more: psychology of productivity, tips on problem solving etc.