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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.
Julian de Ruiter
Julian is a machine learning engineer at GoDataDriven, who also enjoys dabbling in developing open source software. He previously studied at the Delft University of Technology, where he completed his Bachelor in Computer Science and his Master in Bioinformatics cum laude. After Delft, he spent his PhD exploring breast cancer development and origins of (acquired) treatment resistance at the Netherlands Cancer Institute, after which it made sense for Julian to use his skills in a more applied setting at GoDataDriven.
Julian enjoys learning new technologies and finding new ways to apply these technologies to effectively solve real-life problems. In his spare time, he also enjoys developing his own Python packages and contributing to open-source projects. Besides this, he is also co-author of the ‘Data Pipelines with Apache Airflow’ book, which explains how to effectively use Airflow to build data processing platforms and applications.
Timo always strives to improve the quality his work and helping others to do the same. He has experience in giving courses on Python, Airflow and many and other technical topics.