Domain-Specific Fine-Tuning for Finance Training Course

This instructor-led training in Malaysia focuses on advanced fine-tuning and prompt management within Vertex AI. It is designed to help intermediate to advanced practitioners refine Gemini models, establish robust evaluation workflows, and deploy dependable generative AI solutions tailored for real-world enterprise environments.

This instructor-led, live training in Malaysia (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 Malaysia (online or onsite) is designed for advanced-level AI maintenance engineers and MLOps professionals who wish to implement robust continuous 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 continuous learning workflows for deployed models.
• Mitigate catastrophic forgetting through proper training 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 Malaysia (online or onsite) is aimed at 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 of deploying fine-tuned models into production.
• Containerize and deploy models using tools like Docker and Kubernetes.
• Implement monitoring and logging for deployed models.
• Optimize models for latency and scalability in real-world scenarios.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate to advanced professionals who wish to customize 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.
• Optimize model performance and address common challenges.

This instructor-led live training in Malaysia (online or on-site) targets intermediate-level developers and AI professionals seeking 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 core principles of Low-Rank Adaptation (LoRA).
• Implement LoRA for efficient fine-tuning of large models.
• Optimize fine-tuning for resource-constrained environments.
• Evaluate and deploy LoRA-tuned models for practical applications.

This instructor-led, live training in Malaysia (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.
• Optimize models for real-world applications and performance.

This instructor-led, live training in Malaysia (available 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.

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

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

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

This instructor-led, live training in Malaysia (online or onsite) is aimed at intermediate-level 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 structured and unstructured medical data.

By the end 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 Malaysia (online or on-site) is aimed at advanced-level AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to create specialized 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.
• Optimize and deploy fine-tuned models efficiently.

This instructor-led, live training in Malaysia (online or onsite) targets advanced defense AI engineers and military technology developers aiming to fine-tune deep learning models for autonomous vehicles, drones, and surveillance systems, ensuring they meet rigorous security and reliability standards.

Upon completing 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 dynamic environments and mission profiles.
• Implement robust validation and fail-safe mechanisms within model pipelines.
• Ensure alignment with defense-specific compliance, safety, and security standards.

This instructor-led, live training in Malaysia (online or onsite) is designed for intermediate-level legal technology engineers and AI developers who wish to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research within legal service environments.

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

• Prepare and cleanse legal documents for NLP model fine-tuning.
• Implement fine-tuning strategies to enhance model accuracy for legal tasks.
• Deploy models to support contract review, classification, and research.
• Ensure compliance, auditability, and traceability of AI outputs in legal settings.

This instructor-led, live training in Malaysia (online or onsite) targets intermediate to advanced-level machine learning engineers, AI developers, and data scientists who wish to learn how to utilize QLoRA for the efficient fine-tuning of large models tailored to specific tasks and customizations.

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

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

This instructor-led, live training in Malaysia (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.

Upon completing 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.