Domain-Specific Fine-Tuning for Finance Training Course

This instructor-led training in Greece delves into advanced fine-tuning and prompt management within Vertex AI, assisting intermediate and advanced practitioners in optimizing Gemini models, implementing stringent evaluation workflows, and deploying dependable generative AI solutions for real-world enterprise scenarios.

This instructor-led, live training in Greece (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 Greece (online or onsite) is designed for advanced AI maintenance engineers and MLOps professionals who wish to implement robust continual 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 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 Greece (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 Greece (online or onsite) is designed for intermediate to advanced professionals who want 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 session in Greece (online or onsite) is designed for intermediate-level software developers and AI specialists aiming to implement fine-tuning strategies for large models without the need for heavy computational resources.

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

• Gaining insight into the core principles of Low-Rank Adaptation (LoRA).
• Applying LoRA to efficiently fine-tune large models.
• Optimizing fine-tuning processes for resource-constrained settings.
• Assessing and deploying LoRA-enhanced models for real-world applications.

This instructor-led, live training in Greece (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 Greece (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 Greece (online or onsite) is designed for advanced-level data scientists and AI engineers in the financial sector who aim to refine models for applications like credit scoring, fraud detection, and risk modeling using domain-specific financial data.

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

• Fine-tuning AI models on financial datasets to enhance fraud and risk prediction.
• Implementing techniques such as transfer learning, LoRA, and regularization to improve model efficiency.
• Incorporating financial compliance requirements into the AI modeling workflow.
• Deploying fine-tuned models for production use within financial services platforms.

This instructor-led, live training in Greece (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists looking to fine-tune 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 able to:

• Fine-tune AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Apply transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Address privacy concerns, bias, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

This instructor-led, live training in Greece (online or onsite) is designed for 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.

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

• Grasp the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Prepare datasets and preprocess data effectively for fine-tuning.
• Fine-tune DeepSeek LLM for domain-specific applications.
• Optimize and deploy fine-tuned models efficiently.

This guided, real-time training in Greece (online or at a designated location) is designed for senior-level defence AI engineers and military technology specialists who aim to refine deep learning models for deployment in autonomous vehicles, unmanned aerial systems, and surveillance infrastructures, whilst adhering to rigorous security and reliability protocols.

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

• Refining computer vision and sensor integration models for surveillance and targeting operations.
• Adjusting autonomous AI systems to shifting environments and mission parameters.
• Deploying robust validation and fail-safe mechanisms within model workflows.
• Guaranteeing compliance with defence-specific regulations, safety protocols, and security standards.

This instructor-led, live training in Greece (online or onsite) is designed for intermediate-level legal tech engineers and AI developers who aim to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research within legal service environments.

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

• Prepare and clean legal documents for the purpose of fine-tuning NLP models.
• Implement fine-tuning strategies to enhance model accuracy for 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 Greece (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 Greece (online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

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

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantization, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning for task-specific performance.
• Deploy optimized models on real edge hardware platforms.