Machine learning engineering is primarily about patience, attention to detail, and thinking deeply about small things. The day-to-day can be quite tedious & frustrating — but the results of proper execution make it worth it.
MACHINE LEARNING
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100+ Free Data Science Books for Learning
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100+ free data science books: https://
bit.ly/3ApVZdJ credit: @marcusborba -
Build Deep Learning Models in 10 Lines of YAML at PyCon
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Good morning #PyCon ☀️
— Predibase by Rubrik (@predibase) 22 avril 2023
Want to learn how you can build a #deeplearning model in less than 10 lines of #YAML?
Stop by booth 261 to learn more! #PyCon2023 #PyConUS2023 pic.twitter.com/T5XZCVuPdKGood morning #PyCon Want to learn how you can build a #deeplearning model in less than 10 lines of #YAML? Stop by booth 261 to learn more! #PyCon2023 #PyConUS2023
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Guide to Training Your Own Large Language Models
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How to train your own Large Language Models This is a great article on the mechanics of training (your own) large language models. Discusses the importance of training your own LLM, data pipelines, model training, evaluation, and deployment. https://
blog.replit.com/llm-training -
Become a Data Scientist in 30 Days: Evergreen Content Guide
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Sometimes, even I cannot believe the evergreen content I make! Like this video: https://
youtube.com/watch?v=WOIURy
GopJE&ab_channel=AbhishekThakur
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Still valid! Learn how to become a data scientist in 30 days! -
Google AI Models Detect Wildfires Across North America
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Did you know that Google also uses AI models to detect wildfires and has covered more than 30 big wildfire events in the U.S. and Canada, helping inform people and firefighting teams with over 7 million views in Search and Maps? Tap to read https://
bit.ly/3H12wRN -
Apache Spark 3.4 Now Available with Enhanced SQL and PyTorch Support
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Put on your party hats! #ApacheSpark 3.4 is now available for Databricks Runtime 13.0 Users can now: Connect to Spark from any application Increase productivity with new #SQL functionality Do distributed training with PyTorch & more!
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2.5M Developers Use ML Technology in Major Services
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2.5M devs, aka 60-65% of the ML community — including most of the services you use everyday, like Twitter, YouTube, Snap, TikTok, Maps, etc.
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Hugging Face Inference API vs Endpoints Comparison
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What’s the difference between the Hugging Face Inference API, and Inference Endpoints?
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Visual Blocks ML: Low-Code Framework for ML Pipeline Development
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Introducing Visual Blocks ML, a low- to no-code framework that enables users to develop, test, and iterate on #ML pipelines. Learn how it can accelerate ML prototyping so users can launch multimedia applications in production faster → https://t.co/fojdnSQF4O pic.twitter.com/qe8X6fDHLe
— Google AI (@GoogleAI) 21 avril 2023Introducing Visual Blocks ML, a low- to no-code framework that enables users to develop, test, and iterate on #ML pipelines. Learn how it can accelerate ML prototyping so users can launch multimedia applications in production faster → https://
goo.gle/43VUwLK