Model Fine-Tuning Training in Canberra

This instructor-led, live training in Canberra (online or onsite) 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 Canberra (online or on-site) is designed for intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks such as contract analysis, clause extraction, and automated legal research within 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 Canberra (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who wish to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

Upon completion of this training, participants will be able to:

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

This instructor-led, live training in Canberra (online or onsite) targets advanced-level data scientists and AI engineers in the financial sector who wish to refine 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:

• Refine 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 refined models for production use in financial services platforms.

This instructor-led, live training in Canberra (online or onsite) is designed for advanced-level AI maintenance engineers and MLOps professionals who aim to establish robust continual learning pipelines and effective update strategies for deployed, fine-tuned models.

By the conclusion of this training, participants will be capable of:

• Designing and implementing continual learning workflows for deployed models.
• Mitigating catastrophic forgetting through appropriate training techniques and memory management.
• Automating monitoring and update triggers in response to model drift or data changes.
• Integrating model update strategies into existing CI/CD and MLOps pipelines.

This instructor-led, live training in Canberra (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists who want 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 quantization, 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.

This instructor-led, live training in Canberra (online or onsite) is targeted at advanced-level computer vision engineers and AI developers who wish to fine-tune VLMs such as CLIP and Flamingo to improve performance on industry-specific visual-text tasks.

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

• Understand the architecture and pretraining methods of vision-language models.
• Fine-tune VLMs for classification, retrieval, captioning, or multimodal QA.
• Prepare datasets and apply PEFT strategies to reduce resource usage.
• Evaluate and deploy customized VLMs in production environments.

This instructor-led, live training in Canberra (online or onsite) is aimed at intermediate-level ML engineers and AI compliance professionals who wish to identify, evaluate, and reduce safety risks and biases in fine-tuned language models.

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

• Understand the ethical and regulatory context for safe AI systems.
• Identify and evaluate common forms of bias in fine-tuned models.
• Apply bias mitigation techniques during and after training.
• Design and audit models for safety, transparency, and fairness.

This instructor-led, live training in Canberra (online or onsite) is aimed at intermediate-level NLP engineers and knowledge management teams who wish to fine-tune RAG pipelines to enhance performance in question answering, enterprise search, and summarization use cases.

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

• Understand the architecture and workflow of RAG systems.
• Fine-tune retriever and generator components for domain-specific data.
• Evaluate RAG performance and apply improvements through PEFT techniques.
• Deploy optimized RAG systems for internal or production use.

This instructor-led, live training in Canberra (online or onsite) is aimed at intermediate-level ML practitioners and AI developers who wish to fine-tune and deploy open-weight models like LLaMA, Mistral, and Qwen for specific business or internal applications.

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

• Understand the ecosystem and differences between open-source LLMs.
• Prepare datasets and fine-tuning configurations for models like LLaMA, Mistral, and Qwen.
• Execute fine-tuning pipelines using Hugging Face Transformers and PEFT.
• Evaluate, save, and deploy fine-tuned models in secure environments.

This live, instructor-led training in Canberra (available online or on-site) is designed for intermediate-level data scientists and AI engineers looking to fine-tune large language models more affordably and efficiently. The course leverages methods such as LoRA, Adapter Tuning, and Prefix Tuning.

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

• Understand the theoretical foundations of parameter-efficient fine-tuning approaches.
• Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Compare the performance and cost implications of PEFT methods against full fine-tuning.
• Deploy and scale fine-tuned LLMs with reduced compute and storage requirements.

This instructor-led, live training in Canberra (online or onsite) 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 Canberra (online or onsite) is tailored for advanced machine learning engineers and AI researchers seeking to apply RLHF to fine-tune large AI models for superior performance, safety, and alignment.

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

• Comprehend the theoretical foundations of RLHF and its essentiality in modern AI development.
• Implement reward models based on human feedback to direct reinforcement learning processes.
• Fine-tune large language models using RLHF techniques to align outputs with human preferences.
• Apply best practices for scaling RLHF workflows for production-grade AI systems.

This instructor-led, live training in Canberra (online or on-site) is aimed at intermediate-level professionals who wish to gain practical skills in customising AI models for critical 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 Canberra (online or onsite) is aimed at advanced-level professionals who wish to refine their skills in diagnosing and solving fine-tuning challenges for machine learning models.

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

• Diagnose issues like overfitting, underfitting, and data imbalance.
• Implement strategies to improve model convergence.
• Optimize fine-tuning pipelines for better performance.
• Debug training processes using practical tools and techniques.

This instructor-led, live training in Canberra (online or onsite) is aimed at advanced-level professionals who wish to master techniques for optimizing large models for cost-effective fine-tuning in real-world scenarios.

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

• Understand the challenges of fine-tuning large models.
• Apply distributed training techniques to large models.
• Leverage model quantization and pruning for efficiency.
• Optimize hardware utilization for fine-tuning tasks.
• Deploy fine-tuned models effectively in production environments.

This instructor-led, live training in Canberra (online or onsite) targets intermediate-level professionals aiming to leverage prompt engineering and few-shot learning to optimise LLM performance for real-world applications.

Upon completing this training, participants will be able to:

• Grasp the core principles of prompt engineering and few-shot learning.
• Create effective prompts tailored to various NLP tasks.
• Utilise few-shot techniques to adapt LLMs with minimal data requirements.
• Optimise LLM performance for practical, everyday applications.

This instructor-led live training in Canberra (online or onsite) is designed for advanced professionals aiming to master multimodal model refinement 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 Canberra (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 create specialised AI applications tailored to specific industries, domains, or business needs.

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 Canberra (online or onsite) is designed for advanced-level professionals seeking to deploy fine-tuned models reliably and efficiently.

Upon completion of this training, participants will be able to:

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

This instructor-led, live training in Canberra (online or onsite) is designed for 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 Canberra (online or onsite) is aimed at beginner-level to intermediate-level machine learning professionals who wish to understand and apply transfer learning techniques to improve efficiency and performance in AI projects.

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

• Understand the core concepts and benefits of transfer learning.
• Explore popular pre-trained models and their applications.
• Perform fine-tuning of pre-trained models for custom tasks.
• Apply transfer learning to solve real-world problems in NLP and computer vision.

This instructor-led, live training in Canberra (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 Canberra (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 Canberra (online or onsite) is designed for 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.
• Optimize hyperparameters for improved model performance.
• Evaluate and deploy fine-tuned models in real-world scenarios.