MLOps-Specialization-Notes
awesome-mlops
| MLOps-Specialization-Notes | awesome-mlops | |
|---|---|---|
| 7 | 7 | |
| 338 | 3,600 | |
| - | - | |
| 1.4 | 6.8 | |
| 12 months ago | 9 days ago | |
| Python | ||
| - | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MLOps-Specialization-Notes
awesome-mlops
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Choosing an Orchestrator in a green-field setup
Lots of good projects on https://github.com/kelvins/awesome-mlops too
- Software architect with 10 YOE wants to get into AI
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Ask HN: How do you version you GPT prompts
Thanks for pointing towards the right direction. I'll edit the original question.
To rephrase, I am looking for a tool to do model lifecycle management https://github.com/kelvins/awesome-mlops#model-lifecycle and wonder if there is any one in particular that you'd think is better suited for prompts, i.e. an array of objects with templated text
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Run your first Kubeflow pipeline
Recently I've been learning MLOps. There's a lot to learn, as shown by this and this repository listing MLOps references and tools, respectively.
- Awesome list of Libraries and Tools for MLOps
- [D] What are the best resources to crack M L system design interviews?
- Awesome-MLOps: A curated list of MLOps tools
What are some alternatives?
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
awesome-mlops - A curated list of references for MLOps
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
kserve - Standardized Serverless ML Inference Platform on Kubernetes
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
kubeflow-learn
kind - Kubernetes IN Docker - local clusters for testing Kubernetes