AI-Expert-Roadmap
awesome-mlops
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AI-Expert-Roadmap | awesome-mlops | |
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30 | 24 | |
28,418 | 11,719 | |
1.7% | - | |
0.0 | 4.9 | |
4 months ago | about 2 months ago | |
JavaScript | ||
MIT License | - |
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.
AI-Expert-Roadmap
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Best AI ML DL DS Roadmap
**[I.am.ai AI Expert Roadmap](https://i.am.ai/roadmap)**: This roadmap focuses more on AI and includes various aspects of machine learning and deep learning. It's suitable for those who want to delve deeper into AI, particularly in cutting-edge research and applications.
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[D] Best AI ML DL DS Roadmap
Some roadmaps I have found: - [roadmap.sh] AI and Data Scientist Roadmap ← Best? - [i.am.ai] AI Expert Roadmap - [github.com] mrdbourke/machine-learning-roadmap - [github.com] luspr/awesome-ml-courses - [rentry.org] Machine Learning Roadmap
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[D] Roadmap.sh vs Al Expert Roadmap
[i.am.ai] AI Expert Roadmap
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Suggest which roadmap should I follow for ML?
1.https://i.am.ai/roadmap
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Where can I start?
I recommend this site where there's a roadmap for becoming an AI expert: https://i.am.ai/roadmap
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Can some one suggest Top certification courses for AI ?
Currently, I'm also learning about AI from this source https://github.com/AMAI-GmbH/AI-Expert-Roadmap
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How to study Machine Learning effectively?
I suggest following this roadmap. You might be put off by the amount of statistics topics, but it truly underpins almost all of machine learning so it's definitely useful to know.
- Como meterme al mundo AI
- Aprender IA siendo Programador?
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Mlops roadmap
Also: https://i.am.ai/roadmap/
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.
What are some alternatives?
developer-roadmap - Interactive roadmaps, guides and other educational content to help developers grow in their careers.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
cryptocurrency-price-prediction - Cryptocurrency Price Prediction Using LSTM neural network
kserve - Standardized Serverless ML Inference Platform on Kubernetes
dalle-playground - A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
Awesome-Federated-Learning - FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
arbitrary-image-stylization-tfjs - Arbitrary style transfer using TensorFlow.js
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
LeetCode - This is my LeetCode solutions for all 2000+ problems, mainly written in C++ or Python.
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools