The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store development.

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Deep Learningifood_mle_test
Overview

IFood MLE Test

The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store development.

https://github.com/ifood/ifood-data-ml-engineer-test

Projeto: API para servir modelos com Flask, Gunicorn e Docker

Autor: George Rocha

Estrutura do projeto:

.
├── AutoML
│   └── AutoML_h2o.ipynb
├── AWS_infra
│   └── AWS Infrastructure.pdf
├── IFood_API
│   ├── docs
│   │   ├── Document Live.txt
│   │   └── Document Static.html
│   ├── flask_docker
│   │   ├── Dockerfile
│   │   ├── exec.py
│   │   ├── mls.py
│   │   ├── my_app.py
│   │   ├── path.json
│   │   ├── requirements.txt
│   │   ├── setup.py
│   │   └── wsgi.py
│   └── notebook
│       └── example.ipynb
└── READ.me

Installation

Dependencies, this application requires:

Python (>= 3.7)
Docker (= 20.10.12)

Please follow the link bellow for more information on docker:

https://docs.docker.com/engine/install/ubuntu/

Alteração da url de origem dos dados

Para alterar as origens e destinos dos arquivos salvos, favor alterar o arquivo path.json onde:

"modeldata": dados como informações salvas pelo AutoML, info, modelos, arquivos de teste,
"procdata": dados como dados pre processados que serão utilizados para treinar e validar o modelo

Abaixo segue um exemplo:

{	
"modeldata":"https://s3model.blob.core.windows.net/modeldata/",
"procdata":"https://s3model.blob.core.windows.net/prodata/"
}

Execução

No diretório /IFood_ML/IFood_API/flask_docker/ digite no terminal o seguinte comando:

python setup.py

A última linha mostrará a porta que o docker fez o bind com o host. Exemplo:

8000/tcp, :::49171->8000/tcp serene_matsumoto"> 
CONTAINER ID   IMAGE          COMMAND             CREATED         STATUS                  PORTS                                         NAMES
ac5bb0615e0a   flask_docker   "python3 exec.py"   2 seconds ago   Up Less than a second   0.0.0.0:49171->8000/tcp, :::49171->8000/tcp   serene_matsumoto

Documentation

https://app.swaggerhub.com/apis-docs/george53/MLS/1.0.0

AutoML

Executar o notebook IFood_AutoML_h2o no diretório AutoML para criar um modelo, tempo para criação de um minuto na configuração atual.

Exemplo:

Executar o notebook exemplo.ipynb IFood_ML/IFood_API/notebooks para enviar e receber os dados.

Get:

  pd.read_json(requests.get('http://0.0.0.0:49171/').content)

Post:

  r = requests.post('http://0.0.0.0:49171/', data=data).content
  
  prediction = pd.read_json(r)
Owner
George Rocha
George Rocha
GitHub Repository
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