Model Fine-Tuning Training in Delft

This instructor-led live training in Delft (online or onsite) is designed for advanced defense AI engineers and military technology developers who aim to fine-tune deep learning models for use in autonomous vehicles, drones, and surveillance systems while adhering to 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 defense-specific compliance, safety, and security standards.

This instructor-led, live training in Delft (online or onsite) is designed for intermediate-level legal tech engineers and AI developers who want to fine-tune language models for tasks such as 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.

This instructor-led, live training session in Delft (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 both structured and unstructured medical data.

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

• Fine-tune AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Implement transfer learning, domain adaptation, and model compression within medical contexts.
• Navigate privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in practical healthcare settings.

This instructor-led, live training in Delft (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 like 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 improve 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 Delft (online or onsite) is designed for advanced AI maintenance engineers and MLOps professionals looking to implement robust continual learning pipelines and effective update strategies for deployed, fine-tuned models.

Upon completing this training, participants will be able to:

• Design and implement continual learning workflows for deployed models.
• Address catastrophic forgetting through effective 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 Delft (online or on-site) targets intermediate-level embedded AI developers and edge computing specialists who wish to refine and optimize compact AI models for deployment on resource-constrained devices.

Upon completing this training, participants will be capable of:

• Identifying and adapting pre-trained models appropriate for edge implementation.
• Implementing quantization, pruning, and other compression methods to minimize model footprint and latency.
• Refining models via transfer learning to achieve task-specific accuracy.
• Deploying optimized models onto actual edge hardware platforms.

This instructor-led, live training in Delft (online or onsite) is aimed at advanced-level computer vision engineers and AI developers who wish to fine-tune VLMs such as 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 Delft (online or onsite) is designed for intermediate machine learning engineers and AI compliance professionals who wish to detect, assess, and minimize safety risks and biases in fine-tuned language models.

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

• Comprehend the ethical and regulatory framework surrounding safe AI systems.
• Identify and evaluate prevalent forms of bias found in fine-tuned models.
• Implement bias mitigation techniques throughout and after the training phase.
• Design and audit models to ensure safety, transparency, and fairness.

This instructor-led, live training in Delft (online or onsite) is targeted at intermediate-level NLP engineers and knowledge management teams who wish to fine-tune RAG pipelines to enhance performance in question answering, enterprise search, and summarization use cases.

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

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

This instructor-led, live training in Delft (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 key differences between various 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 within secure environments.

This instructor-led, live training in Delft (online or on-site) is designed for intermediate-level data scientists and AI engineers who wish to fine-tune large language models more affordably and efficiently using techniques like LoRA, Adapter Tuning, and Prefix Tuning.

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

• Understand the theoretical underpinnings of parameter-efficient fine-tuning approaches.
• Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Compare performance and cost trade-offs between PEFT methods and full fine-tuning.
• Deploy and scale fine-tuned LLMs with reduced compute and storage requirements.

This instructor-led, live training in Delft (online or onsite) is designed for machine learning engineers, AI developers, and data scientists at an intermediate to advanced level who wish to learn how to utilize 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 session in Delft (online or on-site) is aimed at advanced-level 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 Delft (online or onsite) 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 Delft (online or onsite) is aimed at advanced-level 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 Delft (online or onsite) is aimed at advanced-level professionals who wish to master techniques for optimizing 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 quantization and pruning for efficiency.
• Optimize hardware utilization for fine-tuning tasks.
• Deploy fine-tuned models effectively in production environments.

This instructor-led, live training in Delft (online or onsite) is designed for intermediate-level professionals who wish to leverage prompt engineering and few-shot learning to optimize LLM performance for practical 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 Delft (online or onsite) is designed for advanced professionals aiming to master multimodal fine-tuning for innovative AI solutions.

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

• Grasp the architecture of multimodal models such as CLIP and Flamingo.
• Effectively prepare and preprocess multimodal datasets.
• Fine-tune multimodal models for specific use cases.
• Optimize models for real-world deployment and performance.

This live, instructor-led training session (available online or onsite) is designed for advanced-level AI researchers, machine learning engineers, and developers. The goal is to enable participants to fine-tune DeepSeek LLM models, thereby creating specialized AI applications that address the specific needs of particular industries, domains, or businesses.

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 Delft (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 Delft (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 the Netherlands (online or onsite) is aimed at beginner-level to intermediate-level machine learning professionals who wish to understand and apply transfer learning techniques to improve 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 live, instructor-led training Delft (online or in-person) is designed for intermediate developers and AI practitioners who aim to implement fine-tuning strategies for large models without the need for substantial computational resources.

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

• Grasp the fundamental principles of Low-Rank Adaptation (LoRA).
• Apply LoRA for the efficient fine-tuning of large models.
• Optimize fine-tuning processes within resource-constrained settings.
• Assess and deploy models fine-tuned with LoRA for real-world applications.

This instructor-led live training in Delft (online or onsite) is designed for intermediate to advanced professionals looking 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 Delft (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.