Fine-Tuning Multimodal Models Training Course
This course delves into sophisticated methods for adapting models capable of processing diverse data formats, including text, images, and video. Attendees will acquire knowledge on managing intricate datasets, enhancing model efficiency, and implementing these models in practical scenarios such as visual question answering and content creation.
Delivered as instructor-led live training (available online or in-person), this program targets experienced professionals seeking to master multimodal model refinement for developing cutting-edge AI solutions.
Upon completion of this training, participants will be equipped to:
- Grasp the architecture of multimodal models such as CLIP and Flamingo.
- Effectively prepare and preprocess multimodal datasets.
- Refine multimodal models for designated tasks.
- Optimise models for real-world application and performance.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live laboratory environment.
Customisation Options
- To arrange customised training for this course, please contact us to discuss your requirements.
Course Outline
Introduction to Multimodal Models
- Overview of multimodal machine learning
- Applications of multimodal models
- Challenges in handling multiple data types
Architectures for Multimodal Models
- Exploring models like CLIP, Flamingo, and BLIP
- Understanding cross-modal attention mechanisms
- Architectural considerations for scalability and efficiency
Preparing Multimodal Datasets
- Data collection and annotation techniques
- Preprocessing text, images, and video inputs
- Balancing datasets for multimodal tasks
Fine-Tuning Techniques for Multimodal Models
- Setting up training pipelines for multimodal models
- Managing memory and computational constraints
- Handling alignment between modalities
Applications of Fine-Tuned Multimodal Models
- Visual question answering
- Image and video captioning
- Content generation using multimodal inputs
Performance Optimisation and Evaluation
- Evaluation metrics for multimodal tasks
- Optimising latency and throughput for production
- Ensuring robustness and consistency across modalities
Deploying Multimodal Models
- Packaging models for deployment
- Scalable inference on cloud platforms
- Real-time applications and integrations
Case Studies and Hands-On Labs
- Fine-tuning CLIP for content-based image retrieval
- Training a multimodal chatbot with text and video
- Implementing cross-modal retrieval systems
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Understanding of deep learning concepts
- Experience with fine-tuning pre-trained models
Target Audience
- AI researchers
- Data scientists
- Machine learning practitioners
Open Training Courses require 5+ participants.
Fine-Tuning Multimodal Models Training Course - Booking
Fine-Tuning Multimodal Models Training Course - Enquiry
Fine-Tuning Multimodal Models - Consultancy Enquiry
Provisional Upcoming Courses (Require 5+ participants)
Related Courses
Advanced Fine-Tuning & Prompt Management in Vertex AI
14 HoursVertex AI offers sophisticated tools for fine-tuning large models and managing prompts, empowering developers and data teams to optimise model accuracy, streamline iteration workflows, and ensure rigorous evaluation through built-in libraries and services.
This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners looking to enhance the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.
By the conclusion of this training, participants will be able to:
- Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
- Implement prompt management workflows, including versioning and testing.
- Utilise evaluation libraries to benchmark and optimise AI performance.
- Deploy and monitor improved models within production environments.
Course Format
- Interactive lectures and discussions.
- Hands-on labs focused on Vertex AI fine-tuning and prompt tools.
- Case studies demonstrating enterprise model optimisation.
Course Customisation Options
- To arrange customised training for this course, please contact us.
Advanced Techniques in Transfer Learning
14 HoursThis instructor-led, live training in Australia (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.
Continual Learning and Model Update Strategies for Fine-Tuned Models
14 HoursThis instructor-led, live training in Australia (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.
Deploying Fine-Tuned Models in Production
21 HoursThis instructor-led, live training in Australia (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.
Domain-Specific Fine-Tuning for Finance
21 HoursThis instructor-led, live training in Australia (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.
Fine-Tuning Models and Large Language Models (LLMs)
14 HoursThis instructor-led, live training in Australia (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.
Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)
14 HoursThis instructor-led, live training in Australia (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 for Natural Language Processing (NLP)
21 HoursThis instructor-led, live training in Australia (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.
Fine-Tuning AI for Financial Services: Risk Prediction and Fraud Detection
14 HoursThis instructor-led, live training in Australia (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.
Fine-Tuning AI for Healthcare: Medical Diagnosis and Predictive Analytics
14 HoursThis instructor-led, live training in Australia (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.
Fine-Tuning DeepSeek LLM for Custom AI Models
21 HoursThis instructor-led, live training in Australia (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.
Fine-Tuning Defense AI for Autonomous Systems and Surveillance
14 HoursThis instructor-led, live training in Australia (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.
Fine-Tuning Legal AI Models: Contract Review and Legal Research
14 HoursThis instructor-led, live training in Australia (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.
Fine-Tuning Large Language Models Using QLoRA
14 HoursThis instructor-led, live training in Australia (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.
Fine-Tuning Lightweight Models for Edge AI Deployment
14 HoursThis instructor-led, live training in Australia (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.
