Model Fine-Tuning Training in Uppsala

This instructor-led live training in Uppsala (online or onsite) is designed for senior defense AI engineers and military technology developers who aim to optimize deep learning models for autonomous vehicles, drones, and surveillance systems, ensuring they adhere to strict security and reliability protocols.

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

• Optimize computer vision and sensor fusion models for surveillance and targeting operations.
• Adjust autonomous AI systems to respond effectively to dynamic environments and evolving mission requirements.
• Deploy rigorous validation and fail-safe protocols within model workflows.
• Ensure full compliance with defense-specific safety, security, and regulatory standards.

This instructor-led, live training in Uppsala (online or onsite) is aimed at intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks like 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 in Uppsala (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 Uppsala (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 Uppsala (online or onsite) is designed for advanced AI maintenance engineers and MLOps professionals who aim 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 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 Uppsala (online or onsite) targets intermediate-level embedded AI developers and edge computing specialists who want 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 that are suitable 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 real edge hardware platforms.

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

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

• Grasp the architecture and pretraining methodologies of vision-language models.
• Fine-tune VLMs for tasks like classification, retrieval, captioning, or multimodal QA.
• Prepare datasets and implement PEFT strategies to minimize resource consumption.
• Evaluate and deploy customized VLMs within production environments.

This instructor-led live training in Uppsala (offered online or onsite) is designed for intermediate-level ML engineers and AI compliance professionals seeking to identify, evaluate, and mitigate safety risks and biases in fine-tuned language models.

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

• Grasp the ethical and regulatory context necessary for safe AI systems.
• Identify and assess prevalent forms of bias in fine-tuned models.
• Apply techniques to mitigate bias throughout and after the training process.
• Design and conduct audits on models to ensure safety, transparency, and fairness.

This instructor-led, live training in Uppsala (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 Uppsala (online or onsite) is designed for intermediate-level machine learning practitioners and AI developers who wish to fine-tune and deploy open-weight models such as 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 instructor-led, live training in Uppsala (online or on-site) is designed for intermediate-level data scientists and AI engineers looking to fine-tune large language models more affordably and efficiently, employing techniques like LoRA, Adapter Tuning, and Prefix Tuning.

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

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

This instructor-led, live training in Uppsala (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 Uppsala (online or onsite) is designed for senior 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 Uppsala (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 Uppsala (available online or onsite) is designed for advanced professionals seeking to refine their skills in diagnosing and resolving 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 Uppsala (online or on-site) is designed for advanced professionals seeking to master techniques for optimizing large models for cost-effective fine-tuning in real-world scenarios.

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

• Grasp 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 usage for fine-tuning tasks.
• Effectively deploy fine-tuned models in production environments.

This instructor-led, live training in Uppsala (online or onsite) targets intermediate-level professionals who aim 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 Uppsala (online or onsite) is aimed at advanced-level 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 Uppsala (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 Uppsala (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 Uppsala (online or onsite) is designed for advanced 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 Uppsala (online or on-site) is designed for beginner to intermediate machine learning professionals who aim to understand and implement transfer learning techniques to enhance efficiency and performance in AI projects.

Upon completing this training, participants will be able to:

• Grasp the fundamental ideas and advantages of transfer learning.
• Investigate widely used pre-trained models and their respective use cases.
• Fine-tune pre-trained models to suit specific requirements.
• Utilize transfer learning to address practical challenges in NLP and computer vision.

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

• Grasp 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 Uppsala (online or onsite) targets intermediate-level professionals who aim 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 fundamentals of fine-tuning for NLP tasks.
• Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
• Optimize hyperparameters to improve model performance.
• Evaluate and deploy fine-tuned models in real-world scenarios.