The principal objective of this project is to evaluate the applicant's ability to learn new skills on the fly, build machine learning models in adherence to best practices and colaborate with others.
The applicant is also expected to write a modular code following good coding practices.
Below is a list of tasks that candidates concurently work on. If you deem your contribution to be complete, you can create a pull request.
The team will review your contribution and provide feedback. If its good your branch will be merged with the main branch.
Tasks that are done will be ommited and new tasks will apear for others.
Candidates with merged changes will be invited to pass an interview with the team.
Both students looking for an internship at BIGmama and professionals looking for a full-time position can apply.
- GaussianProcess.py: Write a
GaussianProcessclass that embodies the Gaussian process regression model's functionality. - kernels.py: Implement a selection of three kernel functions.
- Kernel Operations: Enable your kernels to perform addition (
+) and multiplication (*) operations. - Fit the guassian process: Fit your Gaussian process to the datasets provided and plot the results.
- REST API via FastAPI: Design a REST API using FastAPI to make your Gaussian process regression accessible over HTTP.
- Dockerization: Containerize your application with Docker, ensuring all dependencies are included for seamless setup and deployment.
- Documentation: Document your solution thoroughly with docstrings, inline comments, and a
readme.mdfile detailing setup and usage.
Clone the repository
git clone git@github.com:BIGmama-technology/Hiring-AI-engineer.gitRun setup.sh, this will create a virtual environment and install some dependencies
./scripts/setup.shActivate the virtual environment
source .venv/bin/activate- design the structure of your repo in a modular way, example :
.
├── data
│ ├── international-airline-passengers.csv
│ └── mauna_loa_atmospheric_co2.csv
├── docs
│ └── report.pdf
├── LICENSE
├── output
│ └── figure_1.png
├── src
│ ├── __init__.py
│ ├── main.py
│ ├── data
│ │ └── data_loader.py
│ ├── models
│ │ ├── GaussianProcess.py
│ │ └── kernels.py
│ └── utils
│ └── utils.py
├── pyproject.toml
├── README.md
└── setup.cfg
- always use the virtual environment
# activate the virtual environment created by setup.sh
source .venv/bin/activate- Make sure you include any requirements and dependencies in your
pyproject.tomlorrequirements.txt. - Type your code, document it and format it.
# untyped, undocumented and unformatted code
import numpy as np
class gaussiankernel:
def __init__(self,sigma=1.0):
self.sigma=sigma
def compute(self,x1,x2):
return np.exp(-0.5 * np.linalg.norm(x1-x2)**2 / self.sigma**2)# typed, documented and formatted code
import numpy as np
from typing import Any, Union
class GaussianKernel:
def __init__(self, sigma: float = 1.0) -> None:
"""
Initialize the Gaussian kernel with a specified standard deviation (sigma).
Parameters:
sigma (float): The standard deviation of the Gaussian kernel.
"""
self.sigma: float = sigma
def compute(self, x1: Union[float, np.ndarray], x2: Union[float, np.ndarray]) -> Any:
"""
Compute the Gaussian kernel between two points.
Parameters:
x1 (Union[float, np.ndarray]): The first point or vector.
x2 (Union[float, np.ndarray]): The second point or vector.
Returns:
The computed Gaussian kernel value.
"""
return np.exp(-0.5 * np.linalg.norm(x1 - x2) ** 2 / self.sigma ** 2)- Commit often and write meaningful commit messages.
- Create a new branch with your name, push your code to it and create a pull request once you finish your contribution.
Candidates should leverage the following resources for guidance:
- Good practices
- FastAPI Documentation
- Intro to Docker
- What are gaussian processes : interactive guide
- Kernel cookbook
- Packaging with pip
doesn't matter, what important is the value of your contribution and it's quality, impress us !
pick another task, and hurry up !
open an issue and we will answer it as soon as possible !
btawfiq inchalah