Model Fine-Tuning Training in Busan

This instructor-led, live training in Busan (online or on-site) targets senior defense AI engineers and military technology developers who seek to fine-tune deep learning models for autonomous vehicles, drones, and surveillance systems, ensuring adherence to strict security and reliability standards.

Upon completion of this training, participants will be capable of:

• Refining computer vision and sensor fusion models tailored for monitoring and targeting operations.
• Adjusting autonomous AI systems to dynamic environments and varying mission requirements.
• Deploying comprehensive validation and fail-safe mechanisms within model workflows.
• Ensuring strict compliance with defense-specific safety, security, and regulatory standards.

This instructor-led, live training in Busan (online or onsite) is designed for intermediate-level legal tech engineers and AI developers who want to customize language models for tasks such as contract analysis, clause extraction, and automated legal research within legal service environments.

Upon completing this training, participants will be able to:

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

This guided, live training in Busan (online or on-site) is designed for intermediate to advanced medical AI developers and data scientists who aim to adapt models for clinical diagnosis, disease prediction, and patient outcome forecasting using both structured and unstructured medical data.

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

• Fine-tune AI models using healthcare datasets including EMRs, imaging, and time-series data.
• Utilize transfer learning, domain adaptation, and model compression techniques within medical applications.
• Manage privacy concerns, bias issues, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models effectively in real-world healthcare settings.

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

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

• Design and implement continual learning workflows for deployed models.
• Mitigate catastrophic forgetting through effective training and memory management.
• Automate monitoring and update triggers in response to model drift or data changes.
• Integrate model update strategies into existing CI/CD and MLOps pipelines.

This instructor-led, live training in Busan (online or onsite) targets 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.

Upon completing this training, participants will be able to:

• Select and adapt pre-trained models appropriate for edge deployment.
• Apply quantization, pruning, and other compression techniques to minimize model size and latency.
• Fine-tune models using transfer learning to achieve task-specific performance.
• Deploy optimized models on actual edge hardware platforms.

This instructor-led, live training in Busan (available online or on-site) is designed for advanced computer vision engineers and AI developers who want to fine-tune VLMs like 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 Busan (available online or onsite) is tailored for intermediate-level ML engineers and AI compliance professionals seeking 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 Busan (online or onsite) is designed for intermediate-level NLP engineers and knowledge management teams seeking to fine-tune RAG pipelines to improve performance in question answering, enterprise search, and summarization scenarios.

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

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

This instructor-led, live training in Busan (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 Busan (available online or onsite) targets intermediate-level data scientists and AI engineers seeking to fine-tune large language models with improved efficiency and cost savings, leveraging methods like LoRA, Adapter Tuning, and Prefix Tuning.

By the conclusion of this training, participants will have the ability to:

• Understand the theoretical underpinnings of parameter-efficient fine-tuning approaches.
• Implement LoRA, Adapter Tuning, and Prefix Tuning via Hugging Face PEFT.
• Evaluate the performance and cost implications of PEFT methods against full fine-tuning.
• Deploy and scale fine-tuned LLMs with optimized compute and storage usage.

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

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

• Understand the theoretical foundations of RLHF and why it is essential in modern AI development.
• Implement reward models based on human feedback to guide 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 Busan (online or on-site) is aimed at intermediate-level professionals who wish to gain practical skills in customizing 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 Busan (online or onsite) is designed for advanced 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 Busan (online or onsite) is designed for advanced professionals seeking to master techniques for optimizing large models for cost-effective fine-tuning in real-world environments.

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

• Identify the challenges associated with fine-tuning large models.
• Implement distributed training techniques for large models.
• Utilize model quantization and pruning to enhance efficiency.
• Optimize hardware resources for fine-tuning workloads.
• Effectively deploy fine-tuned models in production settings.

This instructor-led, live training in Busan (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimize LLM performance for real-world applications.

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

• Understand the principles of prompt engineering and few-shot learning.
• Design effective prompts for various NLP tasks.
• Leverage few-shot techniques to adapt LLMs with minimal data.
• Optimize LLM performance for practical applications.

This instructor-led, live training in Busan (online or onsite) is designed for 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:

• 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 Busan (online or onsite) targets advanced AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to develop specialized AI applications for 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 Busan (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 Busan (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, offered online or on-site, is designed for beginner to intermediate machine learning professionals aiming to understand and implement transfer learning to boost 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 Busan (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.
• Optimize fine-tuning for resource-constrained environments.
• Evaluate and deploy LoRA-tuned models for practical applications.

This instructor-led live training in Busan (online or onsite) is designed for intermediate to advanced professionals seeking 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 Busan (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:

• Grasp the core principles of fine-tuning for NLP tasks.
• Apply fine-tuning to pre-trained models like GPT, BERT, and T5 for targeted NLP applications.
• Refine hyperparameters to boost model performance.
• Assess and implement fine-tuned models in practical, real-world settings.