Domain-Specific Fine-Tuning for Finance Training Course

This instructor-led training in New Zealand delves into advanced fine-tuning and prompt management within Vertex AI. It supports intermediate and advanced practitioners in optimizing Gemini models, establishing robust evaluation workflows, and deploying dependable generative AI solutions for real-world enterprise environments.

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 onsite) is aimed at intermediate-level to advanced-level professionals who wish to customise pre-trained models for specific 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 advanced-level defence AI engineers and military technology developers who wish to fine-tune deep learning models for use in autonomous vehicles, drones, and surveillance systems while meeting stringent security and reliability standards.

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

• Fine-tune computer vision and sensor fusion models for surveillance and targeting tasks.
• Adapt autonomous AI systems to changing environments and mission profiles.
• Implement robust validation and fail-safe mechanisms in model pipelines.
• Ensure alignment with defence-specific compliance, safety, and security standards.

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.