VAIMLPGOGoogleGO-VAIMLP1.0<p>By the end of the course, learners will be able to:</p> <ul> <li>Understand the key components of Vertex AI and how they work together to support your ML workflows.</li><li>Configure and launch Vertex AI Custom Training and Hyperparameter Tuning Jobs to optimize model performance.</li><li>Organize and version your models using Vertex AI Model Registry for easy access and tracking.</li><li>Configure serving clusters and deploy models for online predictions with Vertex AI Endpoints.</li><li>Operationalize and orchestrate end-to-end ML workflows with Vertex AI Pipelines for increased efficiency and scalability.</li><li>Configure and set up monitoring on deployed models</li></ul><p>Experience building and training custom ML models. Familiar with Docker.</p><p>Machine Learning Engineers, Data Scientists</p><h5>Module 1 - Training, Tuning, and Deploying Models on Vertex AI</h5><ul> <li>Understand Containerized Training Applications</li><li>Understand Vertex AI Custom Training and Tuning Jobs</li><li>Understand how to track and version your trained models in Vertex AI Model Registry</li><li>Understand Online Deployment with Vertex AI Endpoints</li></ul><h5>Module 2 - Orchestrating end-to-end Workflows with Vertex AI Pipelines</h5><ul> <li>Understand Kubeflow</li><li>Understand pre-built and lightweight Python components</li><li>Understand how to compile and execute pipelines on Vertex AI</li></ul><h5>Module 3 - Model Monitoring on Vertex AI</h5><ul> <li>Understand Feature Drift and Skew</li><li>Understand Model Monitoring for models deployed to Vertex AI Endpoints</li></ul>By the end of the course, learners will be able to: - Understand the key components of Vertex AI and how they work together to support your ML workflows. - Configure and launch Vertex AI Custom Training and Hyperparameter Tuning Jobs to optimize model performance. - Organize and version your models using Vertex AI Model Registry for easy access and tracking. - Configure serving clusters and deploy models for online predictions with Vertex AI Endpoints. - Operationalize and orchestrate end-to-end ML workflows with Vertex AI Pipelines for increased efficiency and scalability. - Configure and set up monitoring on deployed modelsExperience building and training custom ML models. Familiar with Docker.Machine Learning Engineers, Data ScientistsModule 1 - Training, Tuning, and Deploying Models on Vertex AI - Understand Containerized Training Applications - Understand Vertex AI Custom Training and Tuning Jobs - Understand how to track and version your trained models in Vertex AI Model Registry - Understand Online Deployment with Vertex AI Endpoints Module 2 - Orchestrating end-to-end Workflows with Vertex AI Pipelines - Understand Kubeflow - Understand pre-built and lightweight Python components - Understand how to compile and execute pipelines on Vertex AI Module 3 - Model Monitoring on Vertex AI - Understand Feature Drift and Skew - Understand Model Monitoring for models deployed to Vertex AI Endpoints1 jour850.00650.00595.00660.00820.00950.00950.00950.00790.00