Wanna learn enough git to be a data scientist?

A hands-on tutorial in 10 steps 👩🏽‍💻👨‍💻↓↓↓ #1 Create your project folder and cd into it

XGBoost is one of the most effective algorithms for time-series prediction.

But, you need to prepare your data carefully.

These are the steps to transform raw data into supervised ML data for time-series prediction ↓ Example

Imagine you work at a ride-sharing app company in NYC as an ML engineer.

You want to help the operations team allocate the fleet of drivers optimally each hour of the day.

The end goal is to maximize revenue

ML Project Idea 💡

Let's find your Twin Celebrity 🔎👸 ↓ Here is a full example, with source code, to learn how to build a complete ML app that finds your Twin Celebrity

Clone the code, modify it, and deploy it!
github.com/SaladSlayer00/…

Training an ML model inside a Jupyter notebook is something every data scientist knows 🏋️

But do you know how to create a real-world ML service that makes a difference for the company you work for? 📈

If the answer is NO, this thread is for you 🤗🧵↓ So, what is the difference between model training and ML service? 🤔

An ML service is a sequence of processing and storage steps that takes in raw data and outputs predictions that are used by the business to make smarter decisions.

Model training is just one of those steps.

ML Project Idea 💡

Let's predict taxi demand in NYC in the next 60 minutes 🚕↓ Business problem 💼

Let's create a predictive model to forecast the number of taxi rides that will happen in Manhattan (New York City)

- in the next hour
- for each taxi zone (e.g. Zone 113 "Lower Manhattan)

Let's do it in 6 steps ↓

Wanna build your first real-world 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁?

Here are 𝟰 𝗙𝗥𝗘𝗘 𝗹𝗲𝗰𝘁𝘂𝗿𝗲𝘀, to put you on the right track 🧵↓ 1️⃣ 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗳𝗼𝗿 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗟𝗠𝘀

In this lecture you will learn
- What is model fine tuning?
- Why is it useful?
- When to use it?
- How to architect a fine tuning pipeline in a real-world project

Are you a data scientist using CSV files to store your data?

What if I told you there is a better way?

Can you imagine a

-> lighter 🦋
-> faster 🏎️
-> cheaper 💸

file format to save your datasets?

Read this thread so you don't need to imagine anymore 👇🏾 Do not get me wrong. I love CSVs.

You can open them with any text editor, inspect them and share them with others.

They have become the standard file format for datasets in the AI/ML community.

However, they have a little problem...

Junior data scientists 𝗔𝗥𝗘 𝗧𝗢𝗟𝗗 the problems they need to solve.
Senior data scientists 𝗙𝗜𝗡𝗗 the problems that need to be solved.

How?

Here is a step-by-step example ↓↓↓ In the real world, data science projects start from a business problem.

They are born to move a key business metric (KPI):

And you, as a data scientist, need to

1 → Understand the business problem

2 → Transform the business problem into a data science problem

3 → Solve it

3 years ago I struggled to land my first freelance ML engineering contract.

Then I discovered this ↓ Building one professional real-world ML project is the best way to stand out from the crowd, and land an ML job.

Here is what I did, 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 👩‍💻👨🏽‍💻↓

XGBoost is one of the most effective algorithms for time-series prediction.

But, you need to prepare your data carefully.

Here is a Python library to help you prepare your data ↓ @joaopcnogueira, one of my students from the Real World ML Tutorial, has built 𝘁𝘀𝟮𝗺𝗹 a Python library that lets you transform

- a time series dataset, into
- a training dataset, with features and targets

Enjoy it

And give it. a star ⭐ on GitHub ↓
github.com/joaopcnogueira…

Wanna learn how to deploy ML models to production?

Let's go step-by-step, with a real-world example 🧵↓ Imagine today is your first day as an ML engineer at Uber, and your task is to improve the ML service that predicts the Estimated Time of Arrival (ETA).

The ETA service gives the end users an estimate of when the driver will arrive at the pickup location.

Wanna build a Machine Learning app that solves a real-world business problem?

Here are the 6 steps to get there ↓↓↓ Step #1. Understand the business problem

Define

→ the business metric you wanna impact (e.g. user retention) and
→ the ML proxy metric you will optimize (e.g. classification accuracy).

There is nothing more frustrating than building the perfect solution to the wrong problem.

ML Project Idea 💡

Let's predict taxi demand in NYC in the next 60 minutes 🚕↓ Business problem 💼

Let's create a predictive model to forecast the number of taxi rides that will happen in Manhattan (New York City)

- in the next hour
- for each taxi zone (e.g. Zone 113 "Lower Manhattan)

Let's do it in 6 steps ↓

Building ML products becomes way easier when you learn these 2 tips

↓↓↓ ➡️ From business problem to ML problem

Every Kaggle competition starts with a clearly defined target metric you need to optimize for.

But, in real-world ML, there is no target metric waiting for you.

Wanna learn MLOps?

Stop reading blog posts.
Build a prediction service instead 🚀

Here is a project you can build (for free) 👩🏽‍💻👨‍💻↓↓↓ Let's build a Machine Learning service to predict the Air Quality Index (AQI) in your city in the next 3 days, using a 100% serverless stack.

You will learn a lot, AND you will build something useful for society.

Win-win 🏆🏆

These are steps to build this ↓

Wanna learn enough git to be a data scientist?

A hands-on tutorial in 10 steps 👩🏽‍💻👨‍💻↓↓↓ #1 Create your project folder and cd into it

Junior data scientists 𝗔𝗥𝗘 𝗧𝗢𝗟𝗗 the problems they need to solve.
Senior data scientists 𝗙𝗜𝗡𝗗 the problems that need to be solved.

How?

Here is a step-by-step example ↓↓↓ In the real world, data science projects start from a business problem.

They are born to move a key business metric (KPI):

And you, as a data scientist, need to

1 → Understand the business problem

2 → Transform the business problem into a data science problem

3 → Solve it

Looking for real-world ML project ideas? 💡

Let's build an ML system that predicts loan defaults.

With 𝗳𝘂𝗹𝗹 𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲👷🏾‍♀️👷↓ Kaggle is full of top-notch tutorials on how to train an ML model.

When it comes to loan default prediction, this is probably the best

kaggle.com/code/faressaya…

Most ML courses teach you how to build the perfect ML model...
... and only then start thinking about deploying it.

And this is why most ML prototypes in real-world projects do not make it into production.

Is there a better way? 🤔

Yes, there is. Let me explain ↓ 🔬 Model-first mindset

A model-first mindset is what Kaggle competitions and most online courses are about.

Your ONLY focus is to build the best possible mapping between a set of input features, and a target metric

And in real-world ML this is often not the best approach...

3 years ago I struggled to land my first freelance ML engineering contract.

Then I discovered this ↓ Building one professional real-world ML project is the best way to stand out from the crowd, and land an ML job.

Here is what I did, 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 👩‍💻👨🏽‍💻↓

What is an 𝗔/𝗕 𝘁𝗲𝘀𝘁 and why do you need to master it as an 𝗠𝗟 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿? An A/B test is a testing strategy, that helps you decide if an ML model is good enough to be used in production.

But, wait a second.

Isn't a low test error (e.g. low mean absolute error) enough to decide if the model is good or not?

Well, nope.

But why?

Because of 2 reasons