Real-world End to End Machine Learning Ops on Google Cloud

Understand the end-to-end ML-ops life cycle on Google Cloud, from problem framing and data understanding to EDA, framework selection, training, deployment, CI/CD, feature stores, and monitoring.

Enable container registry, artifact registry, and cloud build APIs in your GCP account to support the labs, then install Python 3.6+ along with Flask and pytest on your local system.

Demonstrate generating and adapting unit tests for a Flask app with PyTest using ChatGPT, verify 50-row inputs and a 200 response, and run tests locally before deploying to Cloud Run.

Deploy a new version of the same machine learning model by tuning xgboost hyperparameters, saving a model pickle, uploading to a gcs bucket, and routing traffic between cloud run revisions.

Orchestrate end-to-end machine learning pipelines with Airflow or Cloud Composer on GCP to enable continuous training, data validation, retraining, evaluation, and artifact deployment, with metrics in BigQuery and alerts.

Create a GCP composer environment (Airflow 2) and a Vertex AI workbench notebook; enable APIs, set US central 1, and update with requirements.txt via gcloud.

Examine bike share regression model using UCI bikeshare data, with features like date, year, month, day type, workday or weekend, weather, predicting the count with a random forest regressor.

Train a model in Vertex AI, deploy to a model registry, and run batch and online predictions via endpoints, while implementing CI/CD and continuous training on GCP with airflow.

Learn to implement end-to-end ci/cd on Vertex AI using Cloud Build, automating training, model registry upload, and deploying to an endpoint with gcloud commands and cloudbuild.yaml.

Train an xgboost model with multiple hyperparameter combinations using Vertex AI experiments and Kubeflow pipelines, and compare accuracy, precision, and recall across runs.

Run a lab that compares XGBoost, random forest, and logistic regression classifiers using flexible hyperparameters and Kubeflow pipelines in a Jupyter notebook workflow.

Programmatically tune xgboost hyperparameters to maximize accuracy by exploring n_estimators 35–40 and learning_rate 0.2–0.5 for the in-vehicle coupon recommendation model, following the lab assignment.

Deploy a trained model to an endpoint with explainability parameters by uploading to the model registry and using sample attribution with a path count to assess feature contributions.

Deploy a cloud run app that uses the docx library to summarize Word documents in a GCS bucket, via a summarize word documents endpoint with outputs under 50 words.