Optimizing Large Models for Cost-Effective Fine-Tuning Training Course
Optimising large models for fine-tuning is critical to making advanced AI applications feasible and cost-effective. This course focuses on strategies for reducing computational costs, including distributed training, model quantisation, and hardware optimisation, enabling participants to deploy and fine-tune large models efficiently.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to master techniques for optimising 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 quantisation and pruning for efficiency.
- Optimise hardware utilisation for fine-tuning tasks.
- Deploy fine-tuned models effectively in production environments.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised training for this course, please contact us to arrange.
Course Outline
Introduction to Optimising Large Models
- Overview of large model architectures
- Challenges in fine-tuning large models
- Importance of cost-effective optimisation
Distributed Training Techniques
- Introduction to data and model parallelism
- Frameworks for distributed training: PyTorch and TensorFlow
- Scaling across multiple GPUs and nodes
Model Quantisation and Pruning
- Understanding quantisation techniques
- Applying pruning to reduce model size
- Trade-offs between accuracy and efficiency
Hardware Optimisation
- Choosing the right hardware for fine-tuning tasks
- Optimising GPU and TPU utilisation
- Using specialised accelerators for large models
Efficient Data Management
- Strategies for managing large datasets
- Preprocessing and batching for performance
- Data augmentation techniques
Deploying Optimised Models
- Techniques for deploying fine-tuned models
- Monitoring and maintaining model performance
- Real-world examples of optimised model deployment
Advanced Optimisation Techniques
- Exploring low-rank adaptation (LoRA)
- Using adapters for modular fine-tuning
- Future trends in model optimisation
Summary and Next Steps
Requirements
- Experience with deep learning frameworks like PyTorch or TensorFlow
- Familiarity with large language models and their applications
- Understanding of distributed computing concepts
Audience
- Machine learning engineers
- Cloud AI specialists
Open Training Courses require 5+ participants.
Optimizing Large Models for Cost-Effective Fine-Tuning Training Course - Booking
Optimizing Large Models for Cost-Effective Fine-Tuning Training Course - Enquiry
Optimizing Large Models for Cost-Effective Fine-Tuning - Consultancy Enquiry
Provisional Upcoming Courses (Require 5+ participants)
Related Courses
Advanced Fine-Tuning & Prompt Management in Vertex AI
14 HoursVertex 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.
Advanced Techniques in Transfer Learning
14 HoursThis 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.
Continual Learning and Model Update Strategies for Fine-Tuned Models
14 HoursThis 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.
Deploying Fine-Tuned Models in Production
21 HoursThis 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.
Domain-Specific Fine-Tuning for Finance
21 HoursThis 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.
Fine-Tuning Models and Large Language Models (LLMs)
14 HoursThis 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.
Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)
14 HoursThis 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.
Fine-Tuning Multimodal Models
28 HoursThis 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.
Fine-Tuning for Natural Language Processing (NLP)
21 HoursThis 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.
Fine-Tuning AI for Financial Services: Risk Prediction and Fraud Detection
14 HoursThis 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.
Fine-Tuning AI for Healthcare: Medical Diagnosis and Predictive Analytics
14 HoursThis 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.
Fine-Tuning DeepSeek LLM for Custom AI Models
21 HoursThis 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.
Fine-Tuning Defense AI for Autonomous Systems and Surveillance
14 HoursThis 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.
Fine-Tuning Legal AI Models: Contract Review and Legal Research
14 HoursThis 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.
Fine-Tuning Large Language Models Using QLoRA
14 HoursThis 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.
