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Course Outline
Foundational Principles of Generative Artificial Intelligence on Google Cloud
- Definition of generative artificial intelligence and its integration into enterprise operational frameworks
- Standard operational applications for text synthesis, conversational interfaces, content summarization, and search enhancement
- Comprehensive review of Google Cloud generative AI capabilities and the specific function of Vertex AI
- Core architectural concepts including model architecture, prompt engineering, contextual data, and application logic
Implementation and Management of Vertex AI Models
- Navigation of the Google Cloud ecosystem specifically tailored for generative AI initiatives
- Protocols for accessing, configuring, and validating foundation models within Vertex AI
- Comparative analysis of model performance metrics against specific organizational requirements
- Execution of controlled testing environments and systematic review of model-generated responses
Optimization of Prompt Engineering and Output Integrity
- Construction of precise instructions incorporating specific directives, contextual data, and illustrative examples
- Strategies for enhancing response accuracy, structural consistency, tone alignment, and reliability
- Mitigation of common failure modes, including ambiguous outputs and hallucinations
- Iterative refinement processes to optimize prompt effectiveness for professional tasks
Development of Entry-Level Generative AI Applications
- Architectural design for basic workflows supporting chat interfaces, summarization, or content generation
- Integration of user input, prompt logic, and model outputs into cohesive operational flows
- Verification of application functionality through practical laboratory exercises
- Assessment of deployment considerations for real-world production environments
Data Grounding, Performance Evaluation, and Ethical Governance
- The impact of data grounding and organizational context on response reliability and quality
- Fundamental principles of retrieval-augmented generation for knowledge-intensive systems
- Compliance with security standards, data privacy regulations, access management, and responsible AI frameworks on Google Cloud
Transition from Concept Validation to Operational Deployment
- Strategies for evolving proof-of-concept models into robust, scalable business solutions
- Identification of actionable pathways for organizational adoption and team integration
- Course synthesis and directed recommendations for continued professional development
Requirements
- Foundational knowledge of cloud computing principles and standard business application workflows
- Practical experience utilizing the Google Cloud Console or comparable cloud infrastructure platforms
- Proficiency in basic programming or scripting languages
Target Audience
- Developers and technical specialists engaged in the creation of AI-integrated applications
- Cloud engineers and solution architects executing projects within the Google Cloud environment
- Product teams and technical leaders investigating practical generative AI use cases for government and organizational deployment
7 Hours
Testimonials (2)
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt