Model Optimization & Fine-Tuning Training in Oslo

This live, instructor-led training in Oslo (online or in-person) targets advanced defense AI engineers and military technology developers seeking to optimize deep learning models for autonomous vehicles, drones, and surveillance systems while meeting rigorous security and reliability standards.

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

• Optimize computer vision and sensor fusion models for surveillance and targeting operations.
• Adjust autonomous AI systems to adapt to evolving environments and mission requirements.
• Establish reliable validation and fail-safe mechanisms within model workflows.
• Ensure conformity with defense-specific compliance, safety, and security protocols.

This instructor-led, live training in Oslo (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 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 instructor-led, live training in Oslo (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim 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 Oslo (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 Oslo (online or onsite) targets advanced-level AI maintenance engineers and MLOps professionals who wish to implement robust ongoing 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 ongoing 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 guided, live training in Oslo (online or in-person) is designed for intermediate embedded AI engineers and edge computing professionals seeking to refine and optimize lightweight AI models for deployment on devices with limited resources.

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

• Identifying and adapting pre-trained models appropriate for edge deployment.
• Utilizing quantization, pruning, and other compression methods to decrease model footprint and latency.
• Refining models via transfer learning to enhance performance for specific tasks.
• Deploying optimized models onto actual edge hardware platforms.

This instructor-led, live training in Oslo (online or on-site) targets advanced computer vision engineers and AI developers who aim to fine-tune VLMs such as CLIP and Flamingo to enhance 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 Oslo (online or onsite) is designed for intermediate-level machine learning engineers and AI compliance professionals who aim to identify, assess, 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 Oslo (online or onsite) is aimed 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 Oslo (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.

Offered as an interactive, instructor-led live training in Oslo (either online or onsite), this program is designed for intermediate-level data scientists and AI engineers aiming to fine-tune large language models with enhanced affordability and efficiency, leveraging methods such as LoRA, Adapter Tuning, and Prefix Tuning.

By the conclusion of this training, participants will be equipped to:

• Comprehend the theoretical principles behind parameter-efficient fine-tuning approaches.
• Execute LoRA, Adapter Tuning, and Prefix Tuning implementations via Hugging Face PEFT.
• Evaluate the performance and cost trade-offs of PEFT methods in contrast to full fine-tuning.
• Deploy and scale fine-tuned LLMs while significantly reducing compute and storage demands.

This instructor-led, live training in Oslo (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 Oslo (online or onsite) targets advanced machine learning engineers and AI researchers who aim to apply RLHF to fine-tune large AI models for improved performance, safety, and alignment.

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

• Understand the theoretical foundations of RLHF and its significance 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 Oslo (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 Oslo (online or onsite) is designed for advanced professionals aiming to refine their capabilities in diagnosing and solving fine-tuning challenges for machine learning models.

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

• Identify problems such as overfitting, underfitting, and skewed data distribution.
• Apply strategies to enhance model convergence.
• Streamline fine-tuning pipelines to achieve superior performance.
• Troubleshoot training workflows using practical tools and methodologies.

This instructor-led, live training in Oslo (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 Oslo (online or onsite) is designed for intermediate-level professionals looking to leverage prompt engineering and few-shot learning to optimize LLM performance for real-world applications.

Upon completion, participants will be able to:

• Grasp the core principles of prompt engineering and few-shot learning.
• Create effective prompts tailored to various NLP tasks.
• Utilize few-shot methods to adapt LLMs requiring minimal data.
• Enhance LLM performance for practical application needs.

This instructor-led, live training in Norway (online or onsite) is designed for advanced professionals aiming to master the fine-tuning of multimodal models for innovative AI solutions.

Upon completing this training, participants will be able to:

• Comprehend the architecture of multimodal models such as CLIP and Flamingo.
• Effectively prepare and preprocess multimodal datasets.
• Fine-tune multimodal models for targeted tasks.
• Optimize models for performance in real-world applications.

This instructor-led, live training in Oslo (online or onsite) 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 Oslo (online or onsite) is designed for 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 Oslo (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 Oslo (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 instructor-led live training in Oslo (online or on-site) is designed for intermediate-level developers and AI practitioners aiming to implement fine-tuning strategies for large models without relying on extensive computational resources.

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

• Understand the underlying principles of Low-Rank Adaptation (LoRA).
• Implement LoRA for the efficient fine-tuning of large models.
• Optimize fine-tuning procedures for resource-constrained environments.
• Evaluate and deploy LoRA-tuned models for practical use cases.

This instructor-led, live training in Oslo (online or onsite) targets intermediate to advanced professionals aiming to customize pre-trained models for specific tasks and datasets.

By the conclusion 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 Oslo (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.