awesome-mlops
A curated list of references for MLOps (by visenger)
Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications. (by GokuMohandas)
| awesome-mlops | Made-With-ML | |
|---|---|---|
| 24 | 51 | |
| 11,738 | 35,656 | |
| - | - | |
| 5.2 | 6.8 | |
| 9 days ago | 5 months ago | |
| Jupyter Notebook | ||
| - | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
awesome-mlops
Posts with mentions or reviews of awesome-mlops.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-07.
- MLOps
-
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
-
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?
-
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?
-
MLOps on GitHub Actions with Cirun
MLOps
- DevOps - where to begin?
-
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
-
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.
Made-With-ML
Posts with mentions or reviews of Made-With-ML.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-25.
-
[D] How do you keep up to date on Machine Learning?
Made With ML
- Open-Source Production Machine Learning Course
-
Advice for switching careers within analytics
- Develop a (simple!) ML project and apply MLOps best practices to it. Ask Chat GPT all of your MLOps questions. I've joined this MLOps community and it has been very helpful to know what path to follow in order to be better at MLOps, thanks to them I arrived at madewithml, but I haven't done it yet. But it covers all the MLOps side.
-
Recommendation for MLOps resources
Hey, I’m also working in ML. Here’s a great resource: https://madewithml.com. Also, check out Noah Gift’s book Practical MLOPs.
- Ask HN: Resource to learn how to train and use ML Models
-
Need help to find resources to learn ml ops
Try replicating this setup: https://madewithml.com/
-
MLops Resources
madewithml
-
Ask HN: How do I get started with MLOps?
There's a really nice website by Goku Mohandas called Made With ML. IMO it is the best practical guide to MLOps out there: https://madewithml.com
Incase you want to dive a little deeper, https://fullstackdeeplearning.com/course/2022/ is also something I have been recommended by folks.
- Resources for Current DE Interested in Learning Data Science
-
Do organizations still need machine learning engineers?
madewithml is pretty sweet, especially the MLOps side of things. It'll give you good skills in how development in Python and deploying ML works.
What are some alternatives?
When comparing awesome-mlops and Made-With-ML you can also consider the following projects:
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
zero-to-mastery-ml - All course materials for the Zero to Mastery Machine Learning and Data Science course.
kserve - Standardized Serverless ML Inference Platform on Kubernetes
mlops-zoomcamp - Free MLOps course from DataTalks.Club
Awesome-Federated-Learning - FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
practical-mlops-book - [Book-2021] Practical MLOps O'Reilly Book
bodywork - ML pipeline orchestration and model deployments on Kubernetes.
Copulas - A library to model multivariate data using copulas.
awesome-mlops vs metaflow
Made-With-ML vs zero-to-mastery-ml
awesome-mlops vs kserve
Made-With-ML vs mlops-zoomcamp
awesome-mlops vs Awesome-Federated-Learning
Made-With-ML vs FLAML
awesome-mlops vs applied-ml
Made-With-ML vs mlops-course
awesome-mlops vs awesome-mlops
Made-With-ML vs practical-mlops-book
awesome-mlops vs bodywork
Made-With-ML vs Copulas