| site | sandpaper::sandpaper_site |
|---|
Already know how to train an ML model in Python but haven't used the cloud? This hands-on workshop gets you running ML/AI workloads on Google Cloud Platform (GCP) — no prior cloud experience required. By the end, you'll be able to move a local training workflow into GCP's Vertex AI platform and take advantage of cloud-scale hardware and managed services.
What you'll learn:
- Cloud-based notebooks — Set up a Vertex AI Workbench notebook as your development environment and cloud controller.
- Data in the cloud — Upload datasets to Cloud Storage and connect them to your training code.
- Scalable model training — Launch custom training jobs on cloud GPUs/CPUs with your own PyTorch (or other framework) code.
- Hyperparameter tuning — Run parallel tuning jobs in Vertex AI to efficiently search for optimal model settings.
- RAG pipelines — Build a retrieval-augmented generation pipeline using Google's Gemini models with grounding via Google Search.
- Cost management — Monitor spending, set budget alerts, and clean up resources to avoid surprise bills.
This workshop assumes you have a fundamental ML/AI background. Specifically, you should be comfortable with:
- Python — writing scripts, using packages like pandas and NumPy. New to Python? See the Intro to Python workshop.
- Core ML/AI concepts — train/test splits, overfitting, loss functions, hyperparameters. New to ML/AI? See the Intro to Machine Learning workshop.
- Training a model — you've trained at least one model in any framework (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.).
- Command line basics — navigating directories, running commands in a terminal.
No prior GCP or cloud experience is required — that's what this workshop teaches.