Introduction to Nano Banana: Lightweight LLMs for Real-World Applications Training Course
Nano Banana is a lightweight large language model framework engineered for efficient, low-cost deployment across devices and enterprise environments.
This instructor-led live training, available online or onsite, targets beginner-level professionals seeking to understand how lightweight LLMs can be deployed for practical, on-device, and cost-efficient applications.
Upon completing this course, participants will be able to:
- Articulate the core concepts behind lightweight LLMs and Nano Banana.
- Identify suitable use cases for on-device and low-cost AI deployment.
- Assess Nano Banana’s capabilities for business and IT scenarios.
- Make informed decisions regarding integration pathways within their organization.
Course Format
- Instructor explanations accompanied by interactive dialogue.
- Practical exercises designed to reinforce key concepts.
- Hands-on exploration of lightweight LLM capabilities.
Course Customization Options
- For tailored versions of this training, please contact us to customize the program.
Course Outline
Introduction to Lightweight LLMs
- Understanding compact model architectures
- The evolution of resource-efficient AI
- Why lightweight models matter for enterprises
Understanding Nano Banana
- Key features and design principles
- Model capabilities and limitations
- How Nano Banana differs from traditional LLMs
Deployment Models and Use Scenarios
- On-device execution and its benefits
- Local versus cloud inference
- Selecting the right deployment path
Practical Applications Across Industries
- Internal automation and knowledge assistance
- Customer-facing use cases
- Operational and compliance-driven scenarios
Integration Fundamentals
- Evaluating system requirements
- Workflow and process considerations
- API and toolchain introduction
Cost Optimization and Efficiency
- Reducing inference costs using compact models
- Balancing performance and resources
- Planning for scalable deployments
Governance, Privacy, and Risk Management
- Ensuring secure on-device execution
- Understanding data boundaries and safeguards
- Alignment with enterprise policies and standards
Preparing for Organizational Adoption
- Building internal capability and readiness
- Assessing business value through pilot projects
- Laying the groundwork for broader rollouts
Summary and Next Steps
Requirements
- A foundational understanding of general IT concepts
- Experience with basic software tools
- Familiarity with data-driven business workflows
Audience
- General IT teams adopting AI capabilities
- Business users interested in practical AI applications
- Technology managers evaluating on-device LLM strategies
Open Training Courses require 5+ participants.
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Lukasz Kowalczyk - Allegro Sp. z o.o.
Course - Google Gemini AI for Data Analysis
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