DevOps-Roadmap
awesome-mlops
DevOps-Roadmap | awesome-mlops | |
---|---|---|
15 | 24 | |
10,035 | 11,885 | |
- | - | |
5.2 | 4.8 | |
20 days ago | 26 days ago | |
Apache License 2.0 | - |
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.
DevOps-Roadmap
-
Is Cybersecurity Field is still in demand?
โ https://github.com/milanm/DevOps-Roadmap
-
How should I start learning/implementing DevOps in data engineering projects?
In DevOps tools I've worked with GitHub + Jenkins, GitLab + k8s, and I'm now primarily working in the Argo Stack. Depending on where you're at technically, you might use something different. IaC is a ust as well, maybe some config management. Generally I've found that as a Data Engineer with a lot of infra/CICD knowledge, I generally get pigeonholed into those positions on a team, so be prepared for that. I really like this roadmap for DevOps , so you can see where your tech skills are at currently, and what you may need to learn. On top of that, you'll need to learn some data tools. Airflow + dbt is hot right now, Argo is sometimes used in MLOps, Azure Data Stack (I'm not familiar with it) seems common, and probably Spark in almost all cases. You can also checkout in visualization tools probably further down the line, I generally stick to something free when learning on my own, Superset or Google Data Studio (Might be Looker Studio now? Not sure, it's been a while). Here's a roadmap for DE too. I love these roadmaps for getting started, but don't let them distract you from exploring a path more appropriate to what you want to achieve. Generally I've found that as a Data Enigneer with a lot of infra/CICD knowledge, I generally get pigeonholed into those positions on a team
- What DevOps tool you wish that existed? I'll create it for free!
- Syllabus for learning DevOps coming from network eng. position
-
What do you do for a living?
DevOps Engineer. Keeping the internet bits flowing is stressful sometimes but there are a lot of perks like working from home and flexible hours. If youโre interested check this out https://github.com/milanm/DevOps-Roadmap
- Getting Started/Distracted
-
Looking for Advice on Intro into DevOps
Here is a useful roadmap with relevant links to learning resources: https://github.com/milanm/DevOps-Roadmap.
- After studying Python, what should I do next? Can somebody please direct me or provide me with a road map?
-
100+ Must Know Github Repositories For Any Programmer
5. DevOps Roadmap
- DevOps Roadmap
awesome-mlops
- 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.
What are some alternatives?
devops-101 - Intro to DevOps from scratch.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
grafana-awesome - a list of awesome Grafana tools & resources, both official and community-built
kserve - Standardized Serverless ML Inference Platform on Kubernetes
Superbus - An azure service bus explorer for macOS users
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
0x4447_product_secure301 - ๐ง A stack that will allow you to redirect one domain over HTTPS to another.
Awesome-Federated-Learning - FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
suivi-bourse - Monitor the stock shares you own with Python and Prometheus !
applied-ml - ๐ Papers & tech blogs by companies sharing their work on data science & machine learning in production.
AspNetCore-Developer-Roadmap - Roadmap to becoming an ASP.NET Core developer in 2024
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools