Intelligence is neither human nor artificial.
It is just augmented.
25.04.2025 09:27
👍 1
🔁 0
💬 0
📌 0
Intelligence is neither human nor artificial.
It is just augmented.
Our brain is like a neural network, and learning is like doing gradient descent.
We do something, compare with what we wanted to get, compute the error, reflect and readjust our internal parameters.
Companies to not look for online-course-certificate collectors.
Companies look for problem solvers.
Find a problem you are interested in, build your own solution, put on Gihtub and explain what you solved, and how you solved it in the README file.
ML projects in the real world are very complex.
Training the model in a Jupyter notebook is the easy part.
Complexity and tech debt pop everywhere:
- data ingestion
- deployment
- re-training
- monitoring
MLOps is a set of best practices that make your life easier.
Behind every great data scientist...
... there is a greater data engineer.
RAG is what happens when your data engineering pipelines work.
Wreck is what happens when they don't.
Plastic is the worst invention of the 20th century
Water falls from the sky.
Clean data doesn't.
Thank you my dear Data Engineer.
Your Python code is not working?
A breakpoint() is all you need.
Press release by the appropriations subcommittee on NIH budget slashing by @delauro.house.gov 🙏
We humans are a wonderful product of life evolution.
Not the owners of planet Earth.
Ever felt like this?
A breakpoint() is all you need.
AI AI AI
MLuele tanto!
I remember the good old days where Torchflow was all you needed to know to be called a Deep Learning expert...
... or was it Tensortorch ?
Never mind
You've written a FastAPI app and you need to deploy it to AWS?
Here is a library that will make your life WAAAAAAAY easier
github.com/Kludex/mangum
The data engineer moves the data.
The data scientist talks to the data.
The ML engineer squeezes the data.
The business loves the data.
$ pip install freetime
There is (very) intelligent life beyond LLMs...
Training ML models on static data is just 1 piece of the puzzle.
Real-word ML systems need 2 more pieces:
-> Feature pipeline -> ingests raw data and produce fresh features.
-> Inference pipeline -> generates fresh predictions from the fresh features and your production model.
You don’t need the latest LLM.
You just need an Excel sheet
US innovates.
China scales.
Europe is on holidays.
...but these type of holidays where your dad cheats on your mom, she asks for a divorce, all happening while you are stuck in traffic trying to leave the city.
Fun fun fun.
And not these type of relaxed and easy going holidays that recharge your batteries, so you can go back to work and do your best...
US innovates.
China scales.
Europe is on holidays*
A good LinkedIn profile can only help you. I recommend you keep it active, by sharing useful insights with the people who follow you.
Without data engineering...
... there is no LLM engineering.
Landing a Machine Learning job is like a game.
You need to play it differently if you wanna win.
❌ No more dreaming of becoming of an ML engineer.
❌ No more watching Youtube videos.
❌ No more reading Twitter threads.
Enough of all that. GO build something TODAY.
GenAI is worth 0 ...
... when your data warehouse is non existent
