Santiago Profile picture
22 Nov, 18 tweets, 3 min read
For one second, let's forget the idea of a central database.

Imagine a product that doesn't store your data. Instead, you keep that information. You allow others to use it at your convenience and close it whenever you want to.

Let's talk about one example. ↓
2. Think about those people that have built excellent profiles using @kaggle.

They have participated in many competitions, shared their knowledge, and built impressive curriculums.

What would happen if Kaggle decides to ban them?
3. This is not science fiction.

Every single company out there can ban you if they decide to do so.

Maybe it is justified, maybe it isn't, but that's beyond the point.

What happens then?
4. If Kaggle bans you, your entire profile and reputation disappear.

There's nothing wrong with Kaggle, and the same thing applies to Twitter, Facebook, or YouTube:

We are one decision away from losing everything we have built on top of these platforms.
5. The fundamental problem here is that these platforms own your information and everything you have built on top of them.

That Grandmaster title exists as long as Kaggle wants.
6. But this doesn't make sense:

You earned those certificates, built that audience, and completed that course.

You should be in charge of this data.

What's the alternative?
7. Let's get back to the Kaggle example.

Imagine if you could store the titles you earn instead of relying on a central database.

Imagine if there was a way for Kaggle to "write" a Grandmaster badge on your personal database.
8. Kaggle could go away tomorrow. Or they might decide not to let you compete anymore.

But they don't own the Grandmaster badge: you own it, and you get to keep it.

All of your work doesn't depend anymore on a third-party company!
9. This is a fundamental promise of web3 and one of the features that will redefine the world in the coming decade.

We will shift from "centrally stored" to "fully decentralized" and "personally owned."

Let's talk about some specifics.
10. Here are some of the essential bits:

You have a place in the blockchain where you can store information. Whatever lives in that place is associated with you.

Have you seen people buying NFTs? Those "ownership certificates" end up in this place under their name.
11. Let's call this place our "wallet."

Everything in our wallet we own, and we can easily prove it.

Now, think about any institution that issues certifications: AWS, Kaggle, Microsoft, Coursera, Udemy, universities, anyone.
12. When you complete a course, or get a certification, or win a competition, the issuing authority could give you a "token" that you can store in your wallet.

Just like an ape JPG, but this time, something that certifies that you completed the work.
13. They don't need to store who you are and your achievements in their database anymore.

Instead, you store the badge in your wallet.

This is enough for you to prove you did the work.
14. These badges could even be smart:

They could automatically expire after a few years.

You won't be able to transfer them to another wallet.

They could even act different depending on other badges you already own.
15. These badges be your key to a club or a prerequisite to access another—more advanced—certification.

A collection of these badges will become your resume, impossible to falsify and readily available for whoever needs it.
16. You can push this idea even further and think about companies issuing badges, encoding your years of service, and the skills you used during that time.

Interviews could become a simple check of your collection of badges.
17. The technology to make most of this happen already exists.

Of course, web3 is just getting started, and it will take some time for the world to catch up, but the possibilities are fascinating.
18. This is just one example of how the simple idea of "owning your data" could change the landscape.

I'm experimenting right now with this very idea. It's exciting, new, and it has taken me some time to fully see the entire picture.

I'll report back with my findings.

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More from @svpino

19 Nov
Deploying a machine learning model is not a trivial task.

Here are some of the questions you may have to answer every time: ↓
1. What's the input format expected by your service?

2. How can we validate the input is valid? What's the appropriate action if it isn't.

3. What transformations are needed to turn the service's input into the model's input?
4. What transformations are needed to turn the model's output into the service's output?

5. Do we need to allow for batch processing of data?

6. How much time do we have to return an answer?
Read 6 tweets
10 Nov
Over the last few months, I've introduced three main improvements to how I build machine learning models.

Keep in mind that my job is focused on Computer Vision, and I mostly use TensorFlow and Keras.

Here are the highlights: ↓
First, I replaced image generators with the tf.data API.

This change alone has had a major impact on training time. But it doesn't stop there:

• My code is much cleaner
• A data pipeline is easily reusable

tf.data is a must.
Together with loading data, I used image generators to perform data augmentation.

Now, I try to make data augmentation part of the model using Keras' preprocessing layers.

These augmentations now happen in the GPU. This is another nice boost!
Read 6 tweets
8 Nov
If I were to start building a career in machine learning today, here is where I'd focus:

1. Python from the get go.
2. Learn how to build software.

I'd take my time here and avoid rushing into the "machine learning" specific stuff.

Something interesting happens here: ↓
A lot of people start learning software development because they want to get into machine learning.

Then they realize that machine learning is not what they care about.

This is great: there are many ways to build a successful career in the software industry.
As soon as you're comfortable, here is what I'd tackle next:

3. Machine learning fundamentals
4. Hands-on machine learning

I like to cover these at the same time, instead of one after the other: learn some theory, then apply it right away.

Something to keep in mind:
Read 12 tweets
2 Nov
Here is the story of one of those hidden issues with machine learning models that books don't tell you about.

This happened in real life: ↓
Imagine you are building a computer vision model.

It goes something like this:

1. Load a dataset of images
2. Train a model with those images
3. Export the final model

Pretty standard stuff.
To make it more specific, let's imagine that you are using OpenCV to load the images from the disk.

Something like the attached screenshot.

Nothing fancy here, right?
Read 11 tweets
29 Oct
A step-by-step guide to your first Computer Vision problem and 10 questions you should answer after that.

No math and no fancy degrees. If you can read Python, you can do this.

If this is your first time looking at this type of problems, my goal is for you to get familiar with some of the high-level ideas.

There will be some hand-waving, but don't worry about that. Focus on the process and the big pieces.
Here is a @DeepnoteHQ notebook with the code and the entire documentation.

You can open it and run it yourself step by step:

deepnote.com/@svpino/MNIST-…
Read 10 tweets
26 Oct
Here is a problem for you to solve:

How many total handshakes will happen between 10 different people assuming everyone handshakes everyone else?

Don't start drawing things on paper. There's a simple way to solve this: ↓
Let's talk about "triangular series" really quick:

Here is an example of one: 1 2 3 4 5.

I know because I can organize these numbers in a triangle like the attached image shows.

Each row has an equivalent number of points (*'s).
Triangular series always start with 1. We can use "n" to denote the highest number of the series.

So in our [1 2 3 4 5] example, n = 5.
Read 10 tweets

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