awesome-mlops
awesome-mlops
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9 days ago | 22 days ago | |
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awesome-mlops
- MLOps
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ML Engineer Roadmap
I'm in the same boat. Data scientist shifting towards ML engineering-MLOps. The guide seems quite complete. I am also doing the ML DevOps engineer, which has end-to-end projects and has been helpful so far. I also feel that most ML engineers will be Mlops too, as most companies will not distinguish between the two, so I try to focus on this part. Here is a quite comprehensive list of resources: https://github.com/visenger/awesome-mlops
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Mlops roadmap
Good Reference: https://github.com/visenger/awesome-mlops (The Link above has so many Guides, It's insane) https://madewithml.com/
- What do data scientists use Docker for?
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Do you wonder why MLOps is not at the same level as DevOps?
I recently did a deep-dive into MLOps for a client, and I've found that https://ml-ops.org/ offers a great overview. Some topics are a bit too "zoomed out", but they still touch on most important aspects of building an end-to-end product. I found it a great starting point when doing research, and picking and choosing some key points from each section + some google helped a lot. Give it a look, you'll probably find some useful things in there.
- Can you guys explain to me what MLOps is?
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MLOps on GitHub Actions with Cirun
MLOps
- DevOps - where to begin?
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JBCNConf 2022: A great farewell
She made mentions to ML-Ops and MLFlow including Vertex AI the GCP implementation. I will post the video as soon as it is available. In the meantime, you can enjoy any other talk from Nerea Luis
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Can Mechanical Engineers become MLOps?
From your post, you seem to be trained for data science for physics modeling, so I'd recommend to get started with https://ml-ops.org/ and for the data engineering part, I found this https://github.com/andkret/Cookbook open source cookbook to be invaluable.
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?
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
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
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
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!
Awesome-Federated-Learning - FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
kubeflow-learn
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
kind - Kubernetes IN Docker - local clusters for testing Kubernetes