Chapter 1. Introducing MLflow]

Getting started with MLflow

$ chmod +x ./run.sh
$ ./run.sh

http://localhost:8888

Developing your first end-to-end pipeline in MLflow

$ cd ~/tmp
$ source stockpred_env/bin/activate
$ pip install mlflow==1.28.*

$ cd ~/tmp/Machine-Learning-Engineering-with-MLflow/Chapter01/stockpred/

$ docker build . -t stockpred
$ mlflow run .
$ mlflow ui

https://docs.conda.io/en/latest/miniconda.html

// Устанавливаю Miniconda3
$ chmod +x ./Miniconda3-latest-Linux-x86_64.sh
$ ./Miniconda3-latest-Linux-x86_64.sh
$ conda update -n base -c defaults conda

$ pip install sklearn

$ mlflow models serve -m ./mlruns/0/b181d1d1b6c04496be50891ce01a0d08/artifacts/model_random_forest/

$ curl http://127.0.0.1:5000/invocations -H 'Content-Type:application/json' -d '{"data":[[1,1,1,1,0,1,1,1,0,1,1,1,0,0]]}'

response:

[1]