design-patterns-for-humans
awesome-mlops
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design-patterns-for-humans
- Ask HN: How to handle Asian-style “Family name first” when designing interfaces
- Cool Github repositories for Everyone
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15 tools and resources every developer should know about in 2022
2. Design patterns for humans
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[OC] My job search as a self-taught software engineer with no professional work experience
For the first point, what really helped me is taking a look at the various design patterns that are usually used. However, do not force a design pattern into code, it should come naturally to you which pattern fits to a problem. A great resource I can recommend is the README.md file on this GitHub project.
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UNITY Question: How would one develop a random loot generation based on rarity/prefix using scriptable objects that effect the item stats without hardcoding each individual item variant?
I'd recommend reading gang of four design patterns https://github.com/kamranahmedse/design-patterns-for-humans
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Testing with NestJS like a Pro
If you want to learn more about design patterns, don't forget to take a look at Design Patterns for Humans, it's an incredible repository with many interesting examples that you can apply when you want to use a design pattern to solve a specific problem.
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Generating Trees Images, Part 2. Geometry, Graphics and DOM
Ideally, we would write a facade for those methods and provide an API like:
- Design Patterns for Humans
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How does cacheing in classes actually work?
https://github.com/kamranahmedse/design-patterns-for-humans#-singleton
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?
Advance-Python-Notes - Reference matrial for the advance python workshop
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
Tech-Interview-Cheat-Sheet - Studying for a tech interview sucks. Here's an open source cheat sheet to help
kserve - Standardized Serverless ML Inference Platform on Kubernetes
data-making-guidelines - :blue_book: Making Data, the DataMade Way
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
Java - All Algorithms implemented in Java
Awesome-Federated-Learning - FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
C-Plus-Plus - Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
KotlinTutorial - Learn Kotlin programming from scratch
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools