Organizations across industries are leveraging machine learning (ML) to gather insights, improve the customer experience, and drive operational efficiencies. However, building an ML application requires large amounts of training data, software and hardware infrastructure to train the ML models, and tools to run real-time inference that scales with demand.

Join this webinar to learn how performance-optimized AI software from the NVIDIA NGC™ catalog helps companies build ML-powered applications faster. Vertex AI Workbench minimizes context switching through native integration of BigQuery and Cloud Storage, supports distributed Spark jobs, and simplifies packaging notebooks and scaling out to larger GPU clusters with a single click.

You’ll also see how to build a machine learning application to predict bike rental durations and use this example as a template for building your own ML applications.

• How to deploy Jupyter Notebooks for AI software and ML models from the NGC catalog on Google Cloud Vertex AI

• How Google Cloud Vertex AI Workbench helps companies build AI over 5X faster through native integrations to access data and run distributed jobs, ready to go pre-built runtimes for frameworks, and ability to package notebooks and run on GPU clusters with one click

• How to accelerate ETL, train an XGBoost model, and use SHAP to gain insights into the predictions made by the model

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