Made With ML

Applied ML · MLOps · Production
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📦  Product	🔢  Data	📈  Modeling
Objective	Annotation	Baselines
Solution	Exploratory data analysis	Experiment tracking
Evaluation	Splitting	Optimization
Iteration	Preprocessing

📝  Scripting	(cont.)	📦  Application	✅  Testing
Organization	Styling	CLI	Code
Packaging	Makefile	API	Data
Documentation	Logging		Models

⏰  Version control	🚀  Production	(cont.)
Git	Dashboard	Feature stores
Precommit	Docker	Workflows
Versioning	CI/CD	Active learning
	Monitoring

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Directory structure

app/
├── api.py        - FastAPI app
└── cli.py        - CLI app
├── schemas.py    - API model schemas
tagifai/
├── config.py     - configuration setup
├── data.py       - data processing components
├── eval.py       - evaluation components
├── main.py       - training/optimization pipelines
├── models.py     - model architectures
├── predict.py    - inference components
├── train.py      - training components
└── utils.py      - supplementary utilities

Documentation for this application can be found here.

Workflows

Use existing model

1. Set up environment.

export venv_name="venv"
make venv name=${venv_name} env="prod"
source ${venv_name}/bin/activate

2. Pull latest model.

dvc pull experiments
tagifai fix-artifact-metadata

3. 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.

Update model (CI/CD)

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.

Update model (manual)

1. Set up the development environment.

export venv_name="venv"
make venv name=${venv_name} env="dev"
source ${venv_name}/bin/activate

2. Pull versioned data.

dvc pull data/tags.json
dvc pull data/projects.json

3. 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.

4. Train a model (and save all it's artifacts) using params from config/params.json and publish metrics to metrics/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 \
    --experiment-name best \
    --run-name model \
    --publish-metrics  # save to metrics/performance.json

5. Predict tags for an input sentence. It'll use the best model saved from train-model but you can also specify a run-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 if you want

6. 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

7. Commit to git This will clean and update versioned assets (data, experiments), run tests, styling, etc.

git add .
git commit -m ""
<precommit (dvc, tests, style, clean, etc.) will execute>
git push origin main

Commands

Application

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

Streamlit dashboard

make streamlit  # streamlit run streamlit/app.py

MLFlow

make mlflow  # mlflow server -h 0.0.0.0 -p 5000 --backend-store-uri experiments/

Mkdocs

make docs  # python -m mkdocs serve

Testing

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"

Start Jupyterlab

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.

FAQ

Why is this free?

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.

Who is the author?

• 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.
• Connect with me on Twitter and LinkedIn

---

To cite this course, please use:

@article{madewithml,
    title  = "Applied ML - Made With ML",
    author = "Goku Mohandas",
    url    = "https://madewithml.com/courses/applied-ml/"
    year   = "2021",
}