Domain-Specific Fine-Tuning for Finance Training Course

This instructor-led training in Vietnam explores advanced fine-tuning and prompt management within Vertex AI. It assists intermediate and advanced practitioners in optimizing Gemini models, establishing robust evaluation workflows, and deploying dependable generative AI solutions for real-world enterprise scenarios.

This instructor-led, live training in Vietnam (online or onsite) is designed for advanced machine learning professionals who aim 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 Vietnam (online or onsite) is designed for advanced-level AI maintenance engineers and MLOps professionals who aim 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 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 Vietnam (online or onsite) targets advanced-level professionals who aim 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 Vietnam (online or onsite) is aimed at intermediate-level to advanced-level 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 Vietnam (online or in-person) is designed for 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.
• Optimize fine-tuning for resource-constrained environments.
• Evaluate and deploy LoRA-tuned models for practical applications.

This instructor-led, live training in Vietnam (online or onsite) targets advanced professionals who wish to master multimodal model fine-tuning for innovative AI solutions.

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

• Gain a deep understanding of multimodal model architectures, such as CLIP and Flamingo.
• Effectively prepare and preprocess multimodal datasets.
• Fine-tune multimodal models for targeted tasks.
• Optimize models to ensure high performance in real-world scenarios.

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

This instructor-led, live training in Vietnam (online or onsite) targets 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 modeling using domain-specific financial data.

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

• Fine-tune AI models on financial datasets for improved fraud and risk prediction.
• Apply techniques such as transfer learning, LoRA, and regularization to enhance 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 Vietnam (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim to refine 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:

• Optimize 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 session in Vietnam (virtual or in-person) targets senior AI researchers, machine learning engineers, and developers aiming to adapt DeepSeek LLM models for crafting specialized AI applications customized to specific industries, domains, or corporate needs.

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

• Comprehend the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Prepare datasets and apply data preprocessing for model adaptation.
• Adapt DeepSeek LLMs for use in specific domain applications.
• Optimize and efficiently deploy the adapted models.

This guided, live training in Vietnam (available online or in-person) is designed for advanced defense AI engineers and military technology developers who seek to optimize deep learning models for autonomous vehicles, drones, and surveillance systems while meeting rigorous security and reliability standards.

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

• Optimize computer vision and sensor fusion models for monitoring and targeting applications.
• Adapt autonomous AI systems to dynamic environments and varying mission requirements.
• Deploy robust validation and fail-safe mechanisms within model workflows.
• Align operations with defense-specific compliance, safety, and security standards.

This instructor-led, live training in Vietnam (online or onsite) targets intermediate-level legal tech engineers and AI developers who aim to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research in legal service environments.

Upon completing this training, participants will be capable of:

• Preparing and cleaning legal documents for NLP model fine-tuning.
• Implementing fine-tuning strategies to enhance model accuracy for legal tasks.
• Deploying models to support contract review, classification, and research.
• Ensuring compliance, auditability, and traceability of AI outputs within legal contexts.

This instructor-led, live training in Vietnam (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 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 Vietnam (online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimize 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 optimized models on real edge hardware platforms.