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Duration 7 hours
Course Outline
Introduction to Vertex AI and Machine Learning Platforms
- Overview of artificial intelligence and machine learning operational workflows
- Introduction to the Google Cloud Vertex AI framework
- Analysis of Vertex AI architectural components and structural elements
- Assessment of Vertex AI’s role in the development and deployment of machine learning solutions
Establishing the Vertex AI Environment
- Configuration of Google Cloud project settings to support Vertex AI for government operations
- Definition of workspaces, resource allocation, and access permissions
- Preparation of datasets and development environments for secure processing
- Navigation of Vertex AI toolsets and user interfaces
Machine Learning Fundamentals within Vertex AI
- Explanation of supervised learning principles
- Analysis of regression and classification model types
- Data preparation protocols for machine learning workflows
- Methods for evaluating model performance and accuracy metrics
Natural Language Processing (NLP) Integration with Vertex AI
- Foundational concepts in Natural Language Processing
- Review of text-based machine learning application scenarios
- Procedures for preparing and processing text data
- Examination of NLP capabilities available in Vertex AI for government use
Construction and Training of Machine Learning Models
- Preparation of training code compatible with Vertex AI standards
- Containerization of machine learning training applications for standardized deployment
- Configuration of training job parameters and resources
- Execution and monitoring of model training processes
Deployment of Machine Learning Models
- Understanding of model deployment workflows and lifecycle management
- Establishment of model endpoints for service interaction
- Deployment of trained models to support predictive services
- Management of deployed models and associated infrastructure resources
Monitoring and Troubleshooting Vertex AI Solutions
- Oversight of training and deployment activities for compliance and stability
- Identification of common configuration discrepancies and errors
- Diagnostics for resolving model execution issues
- Implementation of best practices for reliable and accountable ML workflows
Practical Workshop and Course Evaluation
- Construction of a complete machine learning workflow utilizing Vertex AI
- Execution of training and deployment for a sample model
- Review of core Vertex AI features and functional capabilities
- Discussion of subsequent steps for advanced machine learning development
Requirements
- Foundational knowledge of machine learning principles
Target Audience
- Software engineers
- Professionals with an interest in machine learning
Testimonials (4)
easy steps in ML
John Erick Baltazar - Globe telecom
Course - Vertex AI
Got additional knowledge about Vertex AI/ML.
Jerico Torres - Globe telecom
Course - Vertex AI
Attends to the questions very well and explain things very well
Renzt Racela - Globe telecom
Course - Vertex AI
Overall, the training was very informative, the trainer provided different use case scenario and exercises so we can be familiarized with the Vertex AI application.
