The backbone of my end-to-end machine learning setup:

• A 48-page Field Notes
• Python
• NumPy, Pandas, Matplotlib, OpenCV
• Scikit-Learn, XGBoost
• TensorFlow
• Google Colab, Jupyter, VSCode
• Docker, Flask
• AWS SageMaker
I personally don& #39;t use C/C++.

That doesn& #39;t mean it& #39;s not useful. I know plenty of people in the industry that rely on C/C++ to do their work.

It just means that I personally haven& #39;t needed it. https://twitter.com/OtMa94573968/status/1384499190718365700?s=20">https://twitter.com/OtMa94573...
There are a lot of satellite tools that I have to use depending on the project. Kinesis, Airflow, SQS... the list is endless.

I just tried to list the core of what I need, and it rarely varies. https://twitter.com/sperezlaw/status/1384499852445425666?s=20">https://twitter.com/sperezlaw...
Flask is very light compared to Django.

I& #39;m using it to create a thin RESTful API layer around the models. https://twitter.com/Ethan_Connelly/status/1384547247933829120?s=20">https://twitter.com/Ethan_Con...
FastAPI is great too!

I don& #39;t use FastAPI instead of Flask because I& #39;m currently reusing existing code that wouldn& #39;t make any sense to rewrite. https://twitter.com/imkhubaibraza/status/1384563705959141379?s=20">https://twitter.com/imkhubaib...
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