Machine Learning Engineering with MLflow

https://github.com/webmakaka/Machine-Learning-Engineering-with-MLflow

Возможно, нужно использовать версию:

tensorflow==2.9.2

// Инсталляция miniconda

$ weget https://repo.anaconda.com/miniconda/Miniconda3-py38_4.12.0-Linux-x86_64.sh
$ chmod +x ./Miniconda3-py38_4.12.0-Linux-x86_64.sh

$ ./Miniconda3-py38_4.12.0-Linux-x86_64.sh

$ cd ~/tmp
$ virtualenv stockpred_env
$ source stockpred_env/bin/activate

$ cd ~/tmp
$ git clone https://github.com/webmakaka/Machine-Learning-Engineering-with-MLflow

Section 1: Problem Framing and Introductions

• Chapter 1. Introducing MLflow • Chapter 2, Your Machine Learning Project

Section 2: Model Development and Experimentation

• Chapter 3. Your Data Science Workbench

• Chapter 4. Experiment Management in MLflow

• Chapter 5. Managing Models with MLflow

Section 3: Machine Learning in Production

• Chapter 6. Introducing ML Systems Architecture

• Chapter 7. Data and Feature Management

• Chapter 8. Training Models with MLflow

• Chapter 9. Deployment and Inference with MLflow