Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Module 1: Introduction to AI and Google Gemini
- Definition and scope of Artificial Intelligence (AI)
- Overview of the Google Gemini AI platform and its associated ecosystem
- Distinct capabilities and operational advantages of Gemini compared to competing AI models
- Practical Session: Demonstrating Gemini AI features via the Google AI Studio interface
Module 2: Large Language Model (LLM) Fundamentals
- Core principles underlying large language models
- Architectural design and operational mechanics of Gemini models
- Comparative analysis of Gemini against GPT and other leading industry models
- Instructional Lab: Analyzing tokenization processes and model outputs using standardized test prompts
Module 3: Gemini Initialization and Setup
- Configuration of the required development environment
- Implementation procedures for the Gemini API and Software Development Kit (SDK)
- Management of authentication protocols, access tokens, and API keys
- Technical Exercise: Execution of a foundational Gemini prompt using Python scripting
Module 4: Operationalizing Gemini Models
- Analysis of various Gemini model variants and their specific functional capabilities
- Strategic selection of models for language, image, or multimodal processing requirements
- Procedures for initializing and validating generative model performance
- Applied Exercise: Evaluation and comparison of text-to-text versus image-to-text model outputs
Module 5: Practical Deployment and Operational Use Cases
- Integration of Gemini AI into conversational interfaces and question-and-answer systems
- Development of semantic search engines and automated text summarization tools
- Adherence to ethical AI standards and mitigation of algorithmic bias
- Collaborative Project: Construction of a “Smart Research Assistant” utilizing NotebookLM and Gemini
Module 6: Advanced Configuration and Optimization
- Strategies for prompt engineering and advanced context management
- Utilization of Gemini for automated code generation and debugging processes
- Execution of fine-tuning workflows via Google Cloud Vertex AI
- Practical Session: Adjustment of model behavior through parameter configuration and temperature control
Module 7: Enterprise Application Development and Team Collaboration
- Planning collaborative workflows and establishing project structures
- Integration of Gemini AI with the broader Google Workspace suite (Drive, Docs, Sheets)
- Group Project: Design, development, and deployment of a specialized AI application for public sector or enterprise use
- Conducting peer reviews and analyzing project outcomes
Module 8: Performance Evaluation and Strategic Planning
- Diagnostic procedures for resolving common technical issues in Gemini implementations
- Review of the Gemini API development roadmap and planned future capabilities
- Establishment of best practices for AI governance, security, and scalability
- Conclusion Activity: Synthesis of key operational lessons and assessment of professional applicability
Summary and Implementation Pathways
Requirements
- Foundational knowledge of core AI principles
- Practical experience with API interactions and cloud-based service architectures
- Proficiency in Python programming
Target Audience
- Software Developers
- Data Science Professionals
- Technical Specialists and AI Practitioners
Testimonials (1)
Flow , vibe and topic on presentation
