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๐ฆ Product | ๐ข Data | ๐ Modeling |
Objective | Annotation | Baselines |
Solution | Exploratory data analysis | Experiment tracking |
Evaluation | Splitting | Optimization |
Iteration | Preprocessing |
๐ Scripting | (cont.) | ๐ฆ Application | โ Testing |
Organization | Styling | CLI | Code |
Packaging | Makefile | API | Data |
Documentation | Logging | Models |
โป๏ธ Reproducability | ๐ Production | (cont.) |
Git | Dashboard | Feature stores |
Pre-commit | CI/CD | Workflows |
Versioning | Monitoring | |
Docker |
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app/
โโโ api.py - FastAPI app
โโโ cli.py - CLI app
โโโ schemas.py - API model schemas
tagifai/
โโโ config.py - configuration setup
โโโ data.py - data processing components
โโโ eval.py - evaluation components
โโโ main.py - training/optimization pipelines
โโโ models.py - model architectures
โโโ predict.py - inference components
โโโ train.py - training components
โโโ utils.py - supplementary utilities
Documentation for this application can be found here.
- Set up environment.
export venv_name="venv"
make venv name=${venv_name} env="dev"
source ${venv_name}/bin/activate
- Pull latest model.
dvc pull
- Run Application
make app env="dev"
You can interact with the API directly or explore via the generated documentation at http://0.0.0.0:5000/docs.
Coming soon after CI/CD lesson where the entire application will be retrained and deployed when we push new data (or trigger manual reoptimization/training). The deployed model, with performance comparisons to previously deployed versions, will be ready on a PR to push to the main branch.
- Set up the development environment.
export venv_name="venv"
make venv name=${venv_name} env="dev"
source ${venv_name}/bin/activate
- Pull versioned data and model artifacts.
dvc pull
- Optimize using distributions specified in
tagifai.main.objective
. This also writes the best model's params to config/params.json
tagifai optimize \
--params-fp config/params.json \
--study-name optimization \
--num-trials 100
We'll cover how to train using compute instances on the cloud from Amazon Web Services (AWS) or Google Cloud Platforms (GCP) in later lessons. But in the meantime, if you don't have access to GPUs, check out the optimize.ipynb notebook for how to train on Colab and transfer to local. We essentially run optimization, then train the best model to download and transfer it's artifacts.
- Train a model (and save all it's artifacts) using params from config/params.json and publish metrics to model/performance.json. You can view the entire run's details inside
experiments/{experiment_id}/{run_id}
or via the API (GET
/runs/{run_id}).
tagifai train-model \
--params-fp config/params.json \
-- model-dir model \
--experiment-name best \
--run-name model
- Predict tags for an input sentence. It'll use the best model saved from
train-model
but you can also specify arun-id
to choose a specific model.
tagifai predict-tags --text "Transfer learning with BERT" # test with CLI app
make app env="dev" # run API and test as well
- View improvements Once you're done training the best model using the current data version, best hyperparameters, etc., we can view performance difference.
tagifai diff --commit-a workspace --commit-b HEAD
- Push versioned data and model artifacts.
make dvc
- Commit to git This will clean and update versioned assets (data, experiments), run tests, styling, etc.
git add .
git commit -m ""
git push origin main
make docker # docker build -t tagifai:latest -f Dockerfile .
# docker run -p 5000:5000 --name tagifai tagifai:latest
make app # uvicorn app.api:app --host 0.0.0.0 --port 5000 --reload --reload-dir tagifai --reload-dir app
make app-prod # gunicorn -c config/gunicorn.py -k uvicorn.workers.UvicornWorker app.api:app
make streamlit # streamlit run streamlit/app.py
make mlflow # mlflow server -h 0.0.0.0 -p 5000 --backend-store-uri stores/model/
make docs # python -m mkdocs serve
make great-expectations # great_expectations checkpoint run [projects, tags]
make test # pytest --cov tagifai --cov app --cov-report html
make test-non-training # pytest -m "not training"
python -m ipykernel install --user --name=tagifai
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install @jupyterlab/toc
jupyter lab
You can also run all notebooks on Google Colab.
While this content is for everyone, it's especially targeted towards people who don't have as much opportunity to learn. I firmly believe that creativity and intelligence are randomly distributed but opportunity is siloed. I want to enable more people to create and contribute to innovation.
- I've deployed large scale ML systems at Apple as well as smaller systems with constraints at startups and want to share the common principles I've learned along the way.
- I created Made With ML so that the community can explore, learn and build ML and I learned how to build it into an end-to-end product that's currently used by over 20K monthly active users.
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To cite this course, please use:
@article{madewithml,
title = "Applied ML - Made With ML",
author = "Goku Mohandas",
url = "https://madewithml.com/courses/mlops/"
year = "2021",
}