Fine-Tuning Defense AI for Autonomous Systems and Surveillance Training Course

Vertex AI delivers advanced tools for fine-tuning large-scale models and managing prompts, empowering development and data teams to enhance model accuracy, streamline iteration workflows, and uphold rigorous evaluation standards using built-in libraries and services.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced practitioners seeking to improve the performance and reliability of generative AI applications through supervised fine-tuning, prompt versioning, and evaluation capabilities within Vertex AI.

By the conclusion of this training, participants will be able to:

• Apply supervised fine-tuning techniques to Gemini models within Vertex AI.
• Implement prompt management workflows, including version control and testing.
• Leverage evaluation libraries to benchmark and optimise AI performance.
• Deploy and monitor enhanced models in production environments.

Course Format

• Interactive lectures and group discussions.
• Practical labs featuring Vertex AI fine-tuning and prompt management tools.
• Real-world case studies showcasing enterprise model optimisation.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

This instructor-led, live training in New Zealand (online or onsite) is aimed at advanced-level machine learning professionals who wish to master cutting-edge transfer learning techniques and apply them to complex real-world problems.

By the end of this training, participants will be able to:

• Understand advanced concepts and methodologies in transfer learning.
• Implement domain-specific adaptation techniques for pre-trained models.
• Apply continual learning to manage evolving tasks and datasets.
• Master multi-task fine-tuning to enhance model performance across tasks.

This instructor-led, live training in New Zealand (online or on-site) is designed for advanced-level AI maintenance engineers and MLOps professionals seeking to implement robust continual learning pipelines and effective update strategies for deployed, fine-tuned models.

By the end of this training, participants will be able to:

• Design and implement continual learning workflows for deployed models.
• Mitigate catastrophic forgetting through appropriate training techniques and memory management.
• Automate monitoring and update triggers based on model drift or data changes.
• Integrate model update strategies into existing CI/CD and MLOps pipelines.

This instructor-led, live training in New Zealand (online or on-site) is designed for advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently.

By the end of this training, participants will be able to:

• Understand the challenges involved in deploying fine-tuned models into production.
• Containerise and deploy models using tools such as Docker and Kubernetes.
• Implement monitoring and logging for deployed models.
• Optimise models for latency and scalability in real-world scenarios.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level professionals seeking to gain practical skills in customising AI models for essential financial tasks.

By the end of this training, participants will be able to:

• Understand the fundamentals of fine-tuning for finance applications.
• Leverage pre-trained models for domain-specific tasks in finance.
• Apply techniques for fraud detection, risk assessment, and financial advice generation.
• Ensure compliance with financial regulations such as GDPR and SOX.
• Implement data security and ethical AI practices in financial applications.

This instructor-led, live training in New Zealand (delivered online or onsite) is designed for intermediate to advanced-level professionals who wish to customise pre-trained models for particular tasks and datasets.

By the end of this training, participants will be able to:

• Understand the principles of fine-tuning and its applications.
• Prepare datasets for fine-tuning pre-trained models.
• Fine-tune large language models (LLMs) for NLP tasks.
• Optimise model performance and address common challenges.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level developers and AI practitioners who wish to implement fine-tuning strategies for large models without the need for extensive computational resources.

By the end of this training, participants will be able to:

• Understand the principles of Low-Rank Adaptation (LoRA).
• Implement LoRA for efficient fine-tuning of large models.
• Optimise fine-tuning for resource-constrained environments.
• Evaluate and deploy LoRA-tuned models for practical applications.

This instructor-led, live training in New Zealand (online or onsite) is aimed at advanced-level professionals who wish to master multimodal model fine-tuning for innovative AI solutions.

By the end of this training, participants will be able to:

• Understand the architecture of multimodal models like CLIP and Flamingo.
• Prepare and preprocess multimodal datasets effectively.
• Fine-tune multimodal models for specific tasks.
• Optimise models for real-world applications and performance.

This instructor-led, live training in New Zealand (online or on-site) is aimed at intermediate-level professionals who wish to enhance their NLP projects through the effective fine-tuning of pre-trained language models.

By the end of this training, participants will be able to:

• Understand the fundamentals of fine-tuning for NLP tasks.
• Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
• Optimise hyperparameters for improved model performance.
• Evaluate and deploy fine-tuned models in real-world scenarios.

This instructor-led, live training in New Zealand (online or onsite) is designed for advanced-level data scientists and AI engineers in the financial sector who wish to fine-tune models for applications such as credit scoring, fraud detection, and risk modelling using domain-specific financial data.

By the end of this training, participants will be able to:

• Fine-tune AI models on financial datasets to improve fraud and risk prediction.
• Apply techniques such as transfer learning, LoRA, and regularisation to enhance model efficiency.
• Integrate financial compliance considerations into the AI modelling workflow.
• Deploy fine-tuned models for production use on financial services platforms.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate to advanced-level medical AI developers and data scientists who wish to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using both structured and unstructured medical data.

By the end of this training, participants will be able to:

• Fine-tune AI models on healthcare datasets, including electronic medical records (EMRs), medical imaging, and time-series data.
• Apply transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Address privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

This instructor-led, live training in New Zealand (online or on-site) is designed for advanced-level AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to develop custom AI applications aligned with specific industries, domains, or organisational requirements.

By the end of this training, participants will be able to:

• Understand the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Prepare datasets and preprocess data for fine-tuning.
• Fine-tune DeepSeek LLM for domain-specific applications.
• Optimise and deploy fine-tuned models efficiently.

This instructor-led, live training in New Zealand (online or on-site) is aimed at intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research in legal service environments.

By the end of this training, participants will be able to:

• Prepare and clean legal documents for fine-tuning NLP models.
• Apply fine-tuning strategies to improve model accuracy on legal tasks.
• Deploy models to assist with contract review, classification, and research.
• Ensure compliance, auditability, and traceability of AI outputs in legal contexts.

This instructor-led, live training in New Zealand (online or on-site) is aimed at intermediate-level to advanced-level machine learning engineers, AI developers, and data scientists who wish to learn how to use QLoRA to efficiently fine-tune large models for specific tasks and customisations.

By the end of this training, participants will be able to:

• Understand the theory behind QLoRA and quantisation techniques for LLMs.
• Implement QLoRA in fine-tuning large language models for domain-specific applications.
• Optimise fine-tuning performance on limited computational resources using quantisation.
• Deploy and evaluate fine-tuned models in real-world applications efficiently.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimise lightweight AI models for deployment on resource-constrained devices.

By the end of this training, participants will be able to:

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantisation, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning for task-specific performance.
• Deploy optimised models on real edge hardware platforms.