Software Engineer (ML & LLMs) Challenge

Overview

Welcome to the Software Engineer (ML & LLMs) Application Challenge. In this, you will have the opportunity to get closer to a part of the reality of the role, and demonstrate your skills and knowledge in machine learning and cloud.

Problem

A jupyter notebook (exploration.ipynb) has been provided with the work of a Data Scientist (from now on, the DS). The DS, trained a model to predict the probability of delay for a flight taking off or landing at SCL airport. The model was trained with public and real data, below we provide you with the description of the dataset:

Column	Description
`Fecha-I`	Scheduled date and time of the flight.
`Vlo-I`	Scheduled flight number.
`Ori-I`	Programmed origin city code.
`Des-I`	Programmed destination city code.
`Emp-I`	Scheduled flight airline code.
`Fecha-O`	Date and time of flight operation.
`Vlo-O`	Flight operation number of the flight.
`Ori-O`	Operation origin city code.
`Des-O`	Operation destination city code.
`Emp-O`	Airline code of the operated flight.
`DIA`	Day of the month of flight operation.
`MES`	Number of the month of operation of the flight.
`AÑO`	Year of flight operation.
`DIANOM`	Day of the week of flight operation.
`TIPOVUELO`	Type of flight, I =International, N =National.
`OPERA`	Name of the airline that operates.
`SIGLAORI`	Name city of origin.
`SIGLADES`	Destination city name.

In addition, the DS considered relevant the creation of the following columns:

Column	Description
`high_season`	1 if `Date-I` is between Dec-15 and Mar-3, or Jul-15 and Jul-31, or Sep-11 and Sep-30, 0 otherwise.
`min_diff`	difference in minutes between `Date-O` and `Date-I`
`period_day`	morning (between 5:00 and 11:59), afternoon (between 12:00 and 18:59) and night (between 19:00 and 4:59), based on `Date-I`.
`delay`	1 if `min_diff` > 15, 0 if not.

Challenge

Instructions

Create a repository in github and copy all the challenge content into it. Remember that the repository must be public.
Use the main branch for any official release that we should review. It is highly recommended to use GitFlow development practices. NOTE: do not delete your development branches.
Please, do not change the structure of the challenge (names of folders and files).
All the documentation and explanations that you have to give us must go in the challenge.md file inside docs folder.
To send your challenge, you must do a POST request to: https://advana-challenge-check-api-cr-k4hdbggvoq-uc.a.run.app/software-engineer This is an example of the body you must send:
```
{
  "name": "Juan Perez",
  "mail": "juan.perez@example.com",
  "github_url": "https://github.com/juanperez/latam-challenge.git",
  "api_url": "https://juan-perez.api"
}
```
PLEASE, SEND THE REQUEST ONCE.

If your request was successful, you will receive this message:
```
{
  "status": "OK",
  "detail": "your request was received"
}
```

NOTE: We recommend to send the challenge even if you didn't manage to finish all the parts.

Context:

We need to operationalize the data science work for the airport team. For this, we have decided to enable an API in which they can consult the delay prediction of a flight.

We recommend reading the entire challenge (all its parts) before you start developing.

Part I

In order to operationalize the model, transcribe the .ipynb file into the model.py file:

If you find any bug, fix it.
The DS proposed a few models in the end. Choose the best model at your discretion, argue why. It is not necessary to make improvements to the model.
Apply all the good programming practices that you consider necessary in this item.
The model should pass the tests by running make model-test.

Note:

You cannot remove or change the name or arguments of provided methods.

You can change/complete the implementation of the provided methods.

You can create the extra classes and methods you deem necessary.

Part II

Deploy the model in an API with FastAPI using the api.py file.

The API should pass the tests by running make api-test.

Note:

You cannot use other framework.

Part III

Deploy the API in your favorite cloud provider (we recomend to use GCP).

Put the API's url in the Makefile (line 26).
The API should pass the tests by running make stress-test.

Note:

It is important that the API is deployed until we review the tests.

Part IV

We are looking for a proper CI/CD implementation for this development.

Create a new folder called .github and copy the workflows folder that we provided inside it.
Complete both ci.yml and cd.yml(consider what you did in the previous parts).

dgquintero / challenge_MLE