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6 Jun, 4 tweets, 2 min read
Good systems produce outstanding results.

↓ Some of my recommendations:

• Improve as a developer
• Improve your communication
• Take a course. Take another. Repeat.
• Solve problems. Many of them.
• Teach others.
• Analysis first. Code is secondary.
• Stay curious.
“Tutorial hell” is only when you focus on consumption and neglect production.

Solve problems and put what you learn out there.
Curiosity pushes me to dig deeper. An infinite number of "but why?" questions.

There's something new and interesting on every layer you uncover.

And the more you dig, the better your understanding and the greater your capacity to create something new.

The way I’ve done it:

• Identify who do I want to follow.
• Follow them.

That’s it.

• Ask questions
• Help them when you can
• Show them your work
* Find opportunities to collaborate
• Observe
• Iterate
• Imitate

• • •

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More from @svpino

Santiago Profile picture
8 Jun
The 4 stages of a machine learning project lifecycle:

1. Project scoping
2. Data definition and preparation
3. Model training and error analysis
4. Deployment, monitoring, and maintenance

Here are 29 questions that you can use at each step of the process.

↓
Project scoping

• What problem are we trying to solve?
• Why do we need to solve this problem?
• What are the constraints?
• What are the risks?
• What's the best approach to solving it?
• How do we measure progress?
• What does success look like?
Data definition and preparation

• What data do we need?
• How are we going to get it?
• How frequently does it change?
• Do we trust the source?
• How is this data biased?
• Can we improve it somehow?
• How are we going to clean it?
• How are we going to augment it?
Read 7 tweets
Santiago Profile picture
7 Jun
Here is a photo from the newspaper of a communist island.

I'm the one standing. This was 20 years ago.

I've been developing software for 25+ years, and I've learned a few things.

I didn't have Internet back then, but now that I do, I can share 3 lessons with you:

↓ Image
Look at that photo again.

This was early 2000.

Those were the best computers Cuba had to offer to our Computer Science faculty. Outdated but good enough.

In a country where owning a personal computer was a crime, it was all we had.
One thing was missing: There was no Internet.

I know this might be hard to understand, so I'll rephrase:

We were going through our Computer Science bachelor's with no Internet access.

The entire wealth of information we had fit in a couple of books.
Read 14 tweets
Santiago Profile picture
4 Jun
Many online courses are useless. They will not get you anywhere.

But there are gems out there.

Here is a curated list that will help you build a machine learning career without paying a fortune in tuition fees. ↓
5 specializations and 1 course, all from a single platform: Coursera.

Take these in order, and you'll end up with more than enough ammunition to tackle real-life problems.

Here is your roadmap: svpino.com/a-machine-lear…
If you find this useful, follow me @svpino, and I'll help put some practical machine learning thoughts right on your timeline.

And if you are looking for ideas that don't fit on Twitter, you can join the other 3,200+ subscribers of my newsletter: digest.underfitted.io.
Read 6 tweets
Santiago Profile picture
3 Jun
The machine learning setup I've been using in 2021:

• Python
• NumPy, Pandas, Matplotlib, OpenCV
• Scikit-Learn, XGBoost
• TensorFlow
• Google Colab, Jupyter, VSCode
• Docker, Flask
• AWS SageMaker
• A 48-page Field Notes
This setup hasn't changed in a while, but I plan to introduce something new: Google's Vertex AI.

How can this platform help with production systems?

I guess we'll find out together.

Follow me @svpino for a practical point of view on machine learning stuff.
It doesn’t need to be like this.

Upload the data somewhere else. Maybe to an S3 bucket, Google Drive, anywhere.

Then download it from the Colab notebook.

This will be fast and reproducible every time you restart the notebook.
Read 8 tweets
Santiago Profile picture
3 Jun
If you are looking to 10x your career:

• Algorithm analysis
• Data structures
• Sorting and searching
• Graphs and traverse methods
• Combinatorial search and heuristic methods
• Approximation algorithms

And this book has it all: amzn.to/3rDngoi Image
I'm a Machine Learning Engineer.

A portion of what I do is specifically related to modeling and data. The rest is software engineering.

Foundational knowledge of algorithms and data structures is key for my job.

There's a third edition of the book released last year. If you are planning to buy it, it's probably a good idea to look for that one.
Read 4 tweets
Santiago Profile picture
31 May
A recipe to get a job in machine learning:

• Get foundational knowledge
• Choose an area to specialize
• Start solving problems
• Write about your solutions
• (Networking is a plus)
• Start applying to job postings

Let's talk about getting foundational knowledge. ↓
These are my most basic recommendations:

• Introduction to Python Programming (Udacity)
• Machine Learning Crash Course (Google)
• Machine Learning (Coursera)

If nothing else, these 3 courses will give you most of what you need.

↓
If you don't have a lot of experience with Python, better to spend more time with it:

• Programming with Python (Brilliant)
• The Official Python Tutorial
• Book: Python Crash Course

Start solving problems as soon as you can.

Tutorial → Problem → Repeat.

↓
Read 9 tweets

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