Introduction to Nano Banana: Lightweight LLMs for Real-World Applications Training Course
Nano Banana is a streamlined large language model framework engineered for efficient, cost-effective deployment across various devices and enterprise settings.
This live, instructor-led training, available either online or on-site, is designed for early-career professionals seeking to grasp how lightweight LLMs can be applied in practical, on-device, and budget-conscious scenarios.
Upon completing this course, participants will be equipped to:
- Articulate the fundamental principles underlying lightweight LLMs and the Nano Banana framework.
- Recognise suitable scenarios for deploying AI on-device with minimal cost.
- Assess the potential of Nano Banana within specific business and IT contexts.
- Make well-informed decisions regarding integration strategies within their organisation.
Course Format
- Instructor-led sessions featuring interactive discussion and clarification.
- Practical exercises designed to solidify key learning outcomes.
- Direct engagement with the capabilities of lightweight LLMs.
Customisation Options
- For a bespoke training experience, please get in touch to tailor the programme to your specific needs.
Course Outline
Introduction to Lightweight LLMs
- Examining compact model architectures
- The progression of resource-efficient AI
- The importance of lightweight models for enterprise environments
Understanding Nano Banana
- Core features and underlying design principles
- Exploring model strengths and limitations
- Distinguishing Nano Banana from conventional LLMs
Deployment Models and Use Scenarios
- Benefits of on-device execution
- Comparing local versus cloud-based inference
- Determining the optimal deployment strategy
Practical Applications Across Industries
- Internal automation and knowledge support
- Customer-interfacing use cases
- Operational and compliance-focused scenarios
Integration Fundamentals
- Reviewing system requirements
- Considerations for workflow and process integration
- Introduction to APIs and the toolchain
Cost Optimisation and Efficiency
- Lowering inference costs through compact models
- Balancing performance with resource usage
- Strategising for scalable deployments
Governance, Privacy, and Risk Management
- Safeguarding secure on-device execution
- Understanding data boundaries and protective measures
- Aligning with enterprise policies and standards
Preparing for Organizational Adoption
- Developing internal skills and readiness
- Evaluating business value via pilot projects
- Establishing the foundation for wider rollouts
Summary and Next Steps
Requirements
- A solid grasp of general IT fundamentals
- Proficiency with standard software tools
- Knowledge of data-centric business processes
Target Audience
- General IT teams looking to adopt AI capabilities
- Business professionals interested in real-world AI applications
- Technology leaders evaluating strategies for on-device LLMs
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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