Machine-Learning-Engineering-with-MLflow
Machine Learning Engineering with MLflow, published by Packt
File Explorer
Download Latest Version (.zip)- .gitignore
- Dockerfile
- Makefile
- MLproject
- README.md
- test.csv
- train.py
- chapther_01_introducing_ml_flow.ipynb
- Dockerfile
- run.sh
- bug-fix-ticket-template.md
- epic-ticket-template.md
- feature-ticket-template.md
- spike-ticket-template.md
- task-ticket-template.md
- pull_request_template.md
- wait-for-it.sh
- Dockerfile
- requirements.test.txt
- wait-for-it.sh
- .dockerignore
- Dockerfile
- .dockerignore
- Dockerfile
- init.sql
- Dockerfile
- Dockerfile.dockerignore
- requirements.txt
- conda.yaml
- MLmodel
- mlflow_sample.ipynb
- stockpred_randomizer.ipynb
- __init__.py
- __init__.py
- test_mlflow.py
- __init__.py
- test_version.py
- run_docker_tests.sh
- .dockerignore
- .env
- .gitignore
- .pylintrc
- .travis.yml
- docker-compose.yml
- LICENSE
- Makefile
- README.md
- requirements.txt
- setup.py
- tox.ini
- wait-for-it.sh
- Dockerfile
- requirements.test.txt
- wait-for-it.sh
- .dockerignore
- Dockerfile
- .dockerignore
- Dockerfile
- init.sql
- Dockerfile
- Dockerfile.dockerignore
- requirements.txt
- hyperopt_optimization_logistic_regression_mlflow.ipynb
- mlflow_run_keras.ipynb
- mlflow_run_logistic_regression.ipynb
- mlflow_run_xgboost.ipynb
- retrieve_training_data.ipynb
- training_data.csv
- __init__.py
- __init__.py
- test_mlflow.py
- __init__.py
- test_version.py
- run_docker_tests.sh
- .dockerignore
- .env
- .gitignore
- .pylintrc
- .travis.yml
- docker-compose.yml
- LICENSE
- Makefile
- README.md
- requirements.txt
- setup.py
- tox.ini
- wait-for-it.sh
- Dockerfile
- requirements.test.txt
- wait-for-it.sh
- .dockerignore
- Dockerfile
- .dockerignore
- Dockerfile
- init.sql
- Dockerfile
- Dockerfile.dockerignore
- requirements.txt
- hyperopt_optimization_logistic_regression_mlflow.ipynb
- mlflow_run_keras.ipynb
- models_under_the_hood_mlflow.ipynb
- random_custom_logging_model.ipynb
- training_data.csv
- __init__.py
- __init__.py
- test_mlflow.py
- __init__.py
- test_version.py
- run_docker_tests.sh
- .dockerignore
- .env
- .gitignore
- .pylintrc
- .travis.yml
- docker-compose.yml
- LICENSE
- Makefile
- README.md
- requirements.txt
- setup.py
- tox.ini
- data.csv
- manifest.yaml
- data.csv
- manifest.yaml
- data.csv
- manifest.yaml
- data.csv
- manifest.yaml
- manifest.yaml
- .gitignore
- check_verify_data.py
- conda.yaml
- feature_set_generation.py
- LICENSE
- load_raw_data.py
- main.py
- MLproject
- README.md
- features.csv
- features.parquet
- online_store.db
- registry.db
- add_features.ipynb
- driver_stats.parquet
- feature_store.yaml
- register_features.py
- wait-for-it.sh
- Dockerfile
- requirements.test.txt
- wait-for-it.sh
- .dockerignore
- Dockerfile
- .dockerignore
- Dockerfile
- init.sql
- Dockerfile
- Dockerfile.dockerignore
- requirements.txt
- __init__.py
- __init__.py
- test_mlflow.py
- __init__.py
- test_version.py
- run_docker_tests.sh
- .dockerignore
- .env
- .gitignore
- .pylintrc
- .travis.yml
- docker-compose.yml
- LICENSE
- Makefile
- README.md
- requirements.txt
- setup.py
- tox.ini
- test_predictions.csv
- data.csv
- conda.yaml
- MLmodel
- model.xgb
- feature_importance_weight.json
- feature_importance_weight.png
- .gitignore
- conda.yaml
- evaluate_model.py
- LICENSE
- main.py
- mlflow_db
- MLproject
- README.md
- register_model.py
- train_model.py
- test_predictions.csv
- data.csv
- conda.yaml
- evaluate_model.py
- LICENSE
- main.py
- MLproject
- README.md
- register_model.py
- train_model.py
- .gitignore
- Dockerfile
- mlflow_db
- MLproject
- conda.yaml
- MLmodel
- model.xgb
- feature_importance_weight.json
- feature_importance_weight.png
- Makefile
- mlflow_db
- test_predictions.csv
- data.csv
- .gitignore
- conda.yaml
- evaluate_model.py
- LICENSE
- main.py
- MLproject
- README.md
- register_model.py
- train_model.py
- api_predictions_client.py
- mlflow_db
- input.csv
- output.csv
- .gitignore
- batch_scoring.py
- Dockerfile
- mlflow_db
- MLproject
- conda.yaml
- MLmodel
- model.xgb
- feature_importance_weight.json
- feature_importance_weight.png
- Makefile
- mlflow_db
- bitpred_poc.dbc
- input_prediction.csv
- conda.yaml
- MLmodel
- model.xgb
- feature_importance_weight.json
- feature_importance_weight.png
- ray_serving.py
- reports_model_performance.html
- boston_data_drift_no_mapping.html
- classification_performance.html
- data_and_target_drift.html
- my_report.html
- reports_model_performance.html
- target_drift.html
- input_data_drift.html
- input_data_drift.json
- target_drift.html
- target_drift.json
- data_and_target_drift.html
- filename.json
- monitoring_data_drift_performance.ipynb
- monitoring_models_drifts.ipynb
- monitoring_target_drifts.ipynb
- reference_training_data.csv
- scored_data.csv
- to_score_input_data.csv
- training_data.csv
- mlflow_db
- events.out.tfevents
- catboost_training.json
- learn_error.tsv
- time_left.tsv
- automl_psystock_mlflow.ipynb
- training_data.csv
- automl_fraud_detection_mlflow.ipynb
- automl_psystock_mlflow.ipynb
- credicard.csv
- r_sample.dbc
- __init__.py
- psystock_deployment_plugin.py
- setup.py
- Main.java
- .gitignore
- license.md
- pom.xml
- readme.md
- .gitignore
- LICENSE
- README.md
# Installation Guide
1. Get the code
git clone https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow
Downloads the entire project code from GitHub to your computer.
cd Machine-Learning-Engineering-with-MLflow
Moves into the project folder you just downloaded.
2. Docker
Easy RecommendedPrerequisites
- Git Needed to download the project code from GitHub.
- Docker Desktop Needed to build and run containers. Install it and keep it running in the background.
docker compose -f Chapter03/gradflow/docker-compose.yml up -d --build
Runs the command against the services defined in the compose file.
Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.
3. Maven (Java)
MediumPrerequisites
- Git Needed to download the project code from GitHub.
- JDK (Java) Required to build and run Java projects.
- Maven The build tool used for the mvn command.
β οΈ This is a large repository, so this method may point to an internal sub-package rather than the actual core product. Check the full README as well.
cd Chapter12/psystock-java-example
This project's files live in a subfolder, so move into it first.
mvn clean install
Installs dependencies and builds the project using Maven.
A BUILD SUCCESS message means it worked. The output is created under target/.
4. Python
EasyPrerequisites
β οΈ This is a large repository, so this method may point to an internal sub-package rather than the actual core product. Check the full README as well.
pip install -r Chapter03/gradflow/docker/python-dev/requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
jupyter notebook
Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.
If it runs without errors and prints output in the terminal, it worked.
5. Make
MediumPrerequisites
- Git Needed to download the project code from GitHub.
- Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).
cd Chapter01/stockpred
This project's files live in a subfolder, so move into it first.
make
Compiles the code based on the generated build configuration to produce an executable.
If it finishes without errors, it worked. Try running the generated executable directly.
// repository documentation
Was this content helpful?
(0 ratings)