Domain-Specific Fine-Tuning for Finance Training Course

This instructor-led training on Norway delves into advanced fine-tuning and prompt management within Vertex AI. It assists intermediate and advanced practitioners in optimizing Gemini models, establishing rigorous evaluation workflows, and deploying dependable generative AI solutions tailored for real-world enterprise settings.

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 Norway (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 instructor-led, live training in Norway (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 Norway (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 Norway (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 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 Norway (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.

This instructor-led, live training in Norway (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 Norway (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 Norway (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 live, instructor-led training in Norway (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 Norway (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 Norway (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 guided, live training in Norway (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.