This event is organized together with GoDataDriven.
- Wednesday, March 30th
- 8:30 – 10:30 CET (doors open, breakfast is served, from 8 AM onwards)
- GoDataDriven office, Wibautstraat 200, Amsterdam
What to Expect
Ever wished you had a bigger machine for training fancy machine learning (ML) models? Or needed a bunch of machines to quickly find the best model and parameter settings for your problem? How do you track all these models and select the best one to deploy to your end-users? And how do we keep things reproducible, so we know how any given model was trained and with what data?
Nowadays, many cloud providers offer fancy MLOps suites with tools that promise to help you solve all of these problems. In this code breakfast, we’ll explore Google’s offering, Vertex AI, and see how Google’s tools can help us do scalable and reproducible machine learning in practice.
We assume that you’re familiar with the basics of machine learning and Python development, as these won’t be covered in detail in the tutorial. Familiarity with Google Cloud services is beneficial but not required.
Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI.
Pre-trained APIs for vision, video, natural language, and more Easily infuse vision, video, translation, and natural language ML into existing applications or build entirely new intelligent applications across a broad range of use cases (including Translation and Speech to Text). AutoML enables developers to train high-quality models specific to their business needs with minimal ML expertise or effort. With a centrally managed registry for all datasets across data types (vision, natural language, and tabular).
End-to-end integration for data and AI Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection.
Support for all open source frameworks Vertex AI integrates with widely used open source frameworks such as TensorFlow, PyTorch, and scikit-learn, along with supporting all ML frameworks and artificial intelligence branches via custom containers for training and prediction.