Offered either online or on-site, our instructor-led live Fine-Tuning courses feature interactive, hands-on practice designed to demonstrate how to leverage customized machine learning models to enhance performance for specific tasks, datasets, or applications.
Fine-Tuning training is available as either "online live training" or "onsite live training". The online live option (also referred to as "remote live training") is conducted via an interactive remote desktop. Onsite live training can be delivered directly at your premises in Copenhagen or at NobleProg corporate training centers in Copenhagen.
You can also take bus 26 or 7A and get off around Vesterbros Torv; from there it is a short walk.
By bus
Useful nearby stops include:
Vesterbros Torv (Vesterbrogade) — about 1 minute from the area.
Frederiksberg Allé (Vesterbrogade) — nearby.
Trommesalen (Vesterbrogade) — nearby.
Bus 26 and 7A serve the Vesterbrogade corridor.
København, Copenhagen
NobleProg København, Bredgade 37, København K, denmark, 1260
Near Amalienborg Palace & Royal Theatre.
If you arrive by train
If you arrive at Copenhagen Central Station (København H):
Walk to København H Metro.
Take the M3 or M4 metro toward Kongens Nytorv.
Get off at Kongens Nytorv.
Walk north toward Nyhavn, then continue along Bredgade.
Bredgade 37 is about 500 m / 6 minutes' walk from Kongens Nytorv.
Another easy option
Marmorkirken (Marble Church) Metro Station is also very close to Bredgade 37. The nearby Bredgade area is about 2 minutes' walk from Marmorkirken station.
By bus
There are bus stops around Dronningens Tværgade / Bredgade, so you can also travel by bus and walk the final few minutes.
This instructor-led program in Copenhagen (available online or in-person) is tailored for advanced defense AI engineers and military technology developers seeking to optimize deep learning models for autonomous vehicles, drones, and surveillance systems. The focus remains on fulfilling strict security and reliability benchmarks.
By the conclusion of this training, participants will be equipped to:
Calibrate computer vision and sensor fusion models for surveillance and targeting objectives.
Adapt autonomous AI systems to shifting environments and diverse mission profiles.
Embed robust validation and fail-safe mechanisms within model pipelines.
Ensure full alignment with defense-specific compliance, safety, and security standards.
This instructor-led, live training (online or onsite) is tailored for intermediate-level legal tech engineers and AI developers seeking to fine-tune language models. The focus is on enhancing performance in 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.
This live, instructor-led course in Copenhagen (offered online or on-site) is tailored for intermediate to advanced medical AI developers and data scientists seeking to refine models for clinical diagnosis, disease prediction, and patient outcome forecasting using a mix of structured and unstructured medical data.
By the conclusion of this training, participants will be equipped to:
Tune AI models for healthcare datasets, including EMRs, imaging, and time-series inputs.
Leverage transfer learning, domain adaptation, and model compression in medical settings.
Manage privacy, reduce bias, and adhere to regulatory compliance in model creation.
Deploy and oversee fine-tuned models in operational healthcare environments.
This instructor-led, live course takes place in Copenhagen (online or on-site) and is tailored for senior data scientists and AI engineers in the finance industry. It focuses on fine-tuning models for high-stakes applications such as credit scoring, fraud detection, and risk modelling, leveraging domain-specific financial data.
By the conclusion of the course, participants will be capable of:
Adapting AI models on financial datasets to improve fraud and risk forecasting.
Utilising methods like transfer learning, LoRA, and regularisation to increase model efficiency.
Integrating financial compliance standards into the AI development process.
Deploying fine-tuned models for live use in financial services environments.
This instructor-led, live training in Copenhagen (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 completion, participants will be equipped to:
Architect and execute continual learning workflows for live models.
Address catastrophic forgetting through strategic training techniques and memory management.
Automate monitoring systems and update triggers in response to model drift or data variations.
Embed model update strategies into established CI/CD and MLOps pipelines.
This live, instructor-led training in Copenhagen (offered online or onsite) is tailored for intermediate-level embedded AI developers and edge computing specialists seeking to refine and optimize lightweight AI models for resource-limited hardware.
By the end of the session, participants will have the capability to:
Choose and modify pre-trained models that align with edge deployment needs.
Utilize techniques like quantization and pruning to decrease model footprint and response time.
Apply transfer learning to fine-tune models for specific operational tasks.
This live, instructor-led training, conducted in Copenhagen (online or onsite), is designed for advanced computer vision engineers and AI developers aiming to fine-tune VLMs such as CLIP and Flamingo to enhance performance on industry-specific visual-text tasks.
Upon completing this program, participants will be equipped to:
Grasp the architectural frameworks and pretraining methodologies behind vision-language models.
Adapt VLMs for specific tasks, including classification, retrieval, captioning, or multimodal question answering.
Curate datasets and implement PEFT strategies to optimize resource efficiency.
Assess and deploy customized VLMs within live production environments.
This live, instructor-led training in Copenhagen, delivered either online or onsite, targets 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 session, 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 live, instructor-led training in Copenhagen (offered online or onsite) is tailored for intermediate NLP engineers and knowledge management professionals seeking to refine RAG pipelines for superior performance in question answering, enterprise search, and summarization.
By the end of this program, learners will be capable of:
Comprehending the structural and workflow dynamics of RAG systems.
Tuning retriever and generator elements to align with domain-specific data requirements.
Evaluating system performance and applying optimizations via PEFT techniques.
Deploying refined RAG systems for both internal usage and production environments.
This live, instructor-led program in Copenhagen—delivered online or onsite—targets intermediate ML practitioners and AI developers focused on fine-tuning and deploying open-weight models like LLaMA, Mistral, and Qwen for specific business or internal needs.
By the end of the course, participants will be able to:
Grasp the ecosystem and nuances of various open-source LLMs.
Prepare datasets and fine-tuning configurations for models such as LLaMA, Mistral, and Qwen.
Run fine-tuning pipelines using Hugging Face Transformers and PEFT.
Assess, save, and deploy fine-tuned models in secure environments.
This instructor-led live training, conducted in Copenhagen either online or on-site, is designed for intermediate-level data scientists and AI engineers aiming to fine-tune large language models cost-effectively and efficiently. It focuses on techniques such as LoRA, Adapter Tuning, and Prefix Tuning.
By the end of this training, participants will be able to:
Comprehend the theory underlying parameter-efficient fine-tuning approaches.
Implement LoRA, Adapter Tuning, and Prefix Tuning via Hugging Face PEFT.
Evaluate the trade-offs in performance and cost between PEFT methods and full fine-tuning.
Deploy and scale fine-tuned LLMs with lower compute and storage requirements.
This live, instructor-led training, conducted in Copenhagen (either online or onsite), is designed for intermediate to advanced machine learning engineers, AI developers, and data scientists aiming to learn how to leverage QLoRA for the efficient fine-tuning of large models to address specific tasks and customization needs.
By the conclusion of this training, participants will be equipped to:
Understand the theoretical basis of QLoRA and quantization methods for LLMs.
Implement QLoRA for fine-tuning large language models tailored to specific domains.
Optimize fine-tuning outcomes under limited computational resources through quantization.
Effectively deploy and evaluate fine-tuned models in live application environments.
This instructor-led live course in Copenhagen, delivered either online or on-site, targets advanced machine learning engineers and AI researchers aiming to apply RLHF to refine large AI models for improved performance, safety, and alignment.
By the end of this training, participants will be able to:
Comprehend the theoretical foundations of RLHF and recognize its importance in modern AI development.
Create reward models based on human feedback to direct reinforcement learning processes.
Optimize large language models through RLHF techniques to ensure outputs reflect human preferences.
Apply best practices for scaling RLHF workflows to support production-grade AI systems.
This instructor-led, live training in Copenhagen (available online or on-site) is tailored for intermediate-level professionals aiming to acquire practical expertise in customizing AI models for essential financial tasks.
By the conclusion of this training, participants will be able to:
Comprehend the fundamentals of fine-tuning for financial applications.
Apply pre-trained models to domain-specific financial tasks.
Utilize techniques for fraud detection, risk assessment, and generating financial advice.
Maintain compliance with financial regulations such as GDPR and SOX.
Incorporate data security and ethical AI practices into financial applications.
This live, instructor-led training, available both online and onsite in Copenhagen, is intended for advanced professionals looking to enhance their capabilities in diagnosing and resolving fine-tuning challenges for machine learning models.
By the conclusion of this training, participants will have the ability to:
Diagnose problems such as overfitting, underfitting, and data imbalance.
Implement effective strategies to improve model convergence.
Optimize fine-tuning pipelines for enhanced performance.
Debug training processes by applying practical tools and techniques.
This instructor-led, live training conducted in Copenhagen (either online or onsite) is designed for advanced-level professionals aiming to master the techniques for cost-effective fine-tuning of large models in real-world settings.
By the conclusion of this training, participants will be able to:
Comprehend the challenges associated with fine-tuning large-scale models.
Apply distributed training methods to large models.
Employ model quantization and pruning to improve efficiency.
Optimize hardware usage for fine-tuning tasks.
Successfully deploy fine-tuned models in production environments.
Available in Copenhagen (online or onsite), this instructor-led live training is designed for intermediate-level professionals seeking to leverage prompt engineering and few-shot learning to boost LLM performance in practical applications.
By the end of this training, participants will be able to:
Understand the core principles of prompt engineering and few-shot learning.
Craft effective prompts for diverse NLP tasks.
Utilize few-shot techniques to adapt LLMs with limited data.
This live, instructor-led training in Copenhagen (available online or on-site) is tailored for senior professionals aiming to master the fine-tuning of multimodal models for cutting-edge AI applications.
Upon completion, participants will be able to:
Understand the underlying architecture of multimodal models such as CLIP and Flamingo.
Effectively curate and pre-process multimodal datasets.
Execute fine-tuning strategies for specific task requirements.
Optimize model performance for production environments.
This instructor-led live training, delivered in Copenhagen (online or onsite), targets advanced AI researchers, machine learning engineers, and developers who intend to fine-tune DeepSeek LLM models to develop specialized AI applications adapted to specific industries, domains, or business needs.
By the conclusion of this training, participants will have the ability to:
Comprehend the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
Assemble and preprocess datasets for fine-tuning purposes.
Fine-tune DeepSeek LLMs for domain-specific applications.
Optimize and deploy fine-tuned models with efficiency.
This live, instructor-led training, conducted Copenhagen (either online or on-site), is designed for advanced professionals aiming to deploy fine-tuned models with high reliability and efficiency.
Following this training, participants will be able to:
Navigate the complexities of deploying fine-tuned models into production environments.
Use tools like Docker and Kubernetes to containerize and deploy models.
Set up effective monitoring and logging for live models.
Optimize models for low latency and high scalability in practical scenarios.
This instructor-led, live training in Copenhagen (online or onsite) is targeted at advanced machine learning professionals aiming to excel in cutting-edge transfer learning techniques and apply them to complex real-world scenarios.
By the end of this training, participants will be able to:
Comprehend advanced concepts and methodologies in transfer learning.
Deploy domain-specific adaptation techniques for pre-trained models.
Implement continual learning to manage evolving tasks and datasets.
Perfect multi-task fine-tuning to improve model performance across tasks.
This instructor-led program in Copenhagen delves into advanced fine-tuning and prompt management within Vertex AI. It assists intermediate and advanced professionals in optimizing Gemini models, implementing rigorous evaluation processes, and deploying dependable generative AI solutions suited for real-world enterprise contexts.
This instructor-led live training in Copenhagen (online or on-site) is tailored for machine learning professionals at beginner to intermediate levels who aim to comprehend and utilize transfer learning techniques to enhance efficiency and performance in AI projects.
After completing this training, participants will be capable of:
Understanding the core concepts and benefits of transfer learning.
Exploring popular pre-trained models and their applications.
Performing fine-tuning of pre-trained models for custom tasks.
Applying transfer learning to solve real-world problems in NLP and computer vision.
This live, instructor-led training in Copenhagen (online or onsite) targets intermediate-level developers and AI practitioners 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 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 held in Copenhagen (available online or onsite) is tailored for intermediate to advanced professionals seeking to adapt pre-trained models for specific tasks and datasets.
By the end of the session, participants will be able to:
Comprehend the fundamental principles of fine-tuning and its real-world applications.
Prepare and structure datasets for the fine-tuning of pre-trained models.
Execute fine-tuning on Large Language Models (LLMs) to handle NLP tasks.
Optimize model output and resolve frequent technical challenges.
This instructor-led, live training in Copenhagen (delivered online or onsite) is tailored for intermediate professionals seeking to strengthen their NLP projects via the effective fine-tuning of pre-trained language models.
By the conclusion of this training, participants will have the ability to:
Comprehend the basics of fine-tuning for NLP tasks.
Customize pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
Adjust hyperparameters to boost model performance.
Evaluate and integrate fine-tuned models into real-world scenarios.
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