aws-panorama-samples
applied-ml
aws-panorama-samples | applied-ml | |
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1 | 13 | |
82 | 25,984 | |
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5.9 | 3.0 | |
about 2 months ago | 1 day ago | |
Python | ||
MIT No Attribution | MIT License |
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aws-panorama-samples
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Remote View your Computer Vision Models Running on AWS Panorama
Backpack is open source and available on GitHub. You can build and run the example docker container on an ARM64 based system. For example, suppose you’ve already set up the Panorama Test Utility on a t4g type EC2 instance. In this case, you can build and launch a shell in this container with the following commands on the EC2 instance:
applied-ml
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[D] Favorite ML Youtube Channels/Blogs/Newsletters
Also, have any of you stumbled across any cool GitHub repos like this one: https://github.com/eugeneyan/applied-ml ?
- Curated Papers on Machine Learning in Production
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Top Github repo trends in 2021
The second repo I LOVE is Eugene Yan’s Applied ML repository. This is a brilliant idea to create and actually something I was planning on sort of casually doing in my non-existent free time… Anyhow, it is a curated list of technical posts from top engineering teams (Netflix, Amazon, Pinterest, Linkedin, etc.) detailing how they built out different types of AI/ML systems (e.g. forecasting, recommenders, search and ranking, etc.). Ofc, it focuses on AI/ML, but something similar could be made for the traditional or BI-oriented analytics stack, as well as the streaming world, super high value for practitioners! Btw-one of my favorite things at BCG used to be looking at our IT architecture team’s reference architecture diagrams… the best way to understand technologies is to look at how a ton of stuff is architected… and its fun!
- Curated papers, articles, & blogs on data science and ML in production
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Messed up my career by pivoting to DS. Wondering if it's too late to switch to MLE
Applied ML: A collection of papers, articles, and blogs on ML in production by different companies (Netflix, Uber, Facebook, LinkedIn, etc)
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[D] A dilemma of an ML guy in industry
Eugene Yan's applied-ml has tons of case studies.
- Papers & tech blogs by companies sharing their work on data science & machine learning in production.
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My information dump for people trying to break into data science/interview notes
https://github.com/eugeneyan/applied-ml You may find some of his links interesting. I would avoid anything that refers to scaling up a platform as these are more backend engr focus. The more relevant posts to you are probably on the scale of blog posts that are product oriented like the ones I listed in section 4 (e.g. we wanted to solve X for our users and this is how we scoped and defined it). The technical aspects should come backseat to the business aspects. There's def a lot of companies/blog posts that he missed, but the internet is huge.
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[D] Can anyone point me to resources/case studies of companies/business creating infrastructure for their data needs?
Check the resources mentioned in applied-ml. It includes blog posts/papers from many companies describing how they built some ML product X.
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What content would be useful to intermediate Data Scientist
Check out this repo. They collect hundreds of case studies, broken down by dozens of methodologies from large real-world companies such as AirBnB, Nvidia, Uber, Netflix etc.
What are some alternatives?
awesome-mlops - A curated list of references for MLOps
awesome-ml-blogs - Curated list of technical blogs on machine learning · AI/ML/DL/CV/NLP/MLOps
machine-learning-roadmap - A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
Cookbook - The Data Engineering Cookbook
ml-surveys - đź“‹ Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
pipebase - data integration framework
data-engineering-book - Accumulated knowledge and experience in the field of Data Engineering
PowerToys - Windows system utilities to maximize productivity
stanford-cs-229-machine-learning - VIP cheatsheets for Stanford's CS 229 Machine Learning
awesome-artificial-intelligence-research - A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.
machine_learning_examples - A collection of machine learning examples and tutorials.
ML-YouTube-Courses - đź“ş Discover the latest machine learning / AI courses on YouTube.