Fine-Tuning Training in Athens

This guided, real-time training in Athens (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 Athens (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 Athens (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 Athens (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 Athens (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 Athens (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.

This instructor-led, live training in Athens (online or onsite) is designed for advanced-level computer vision engineers and AI developers seeking 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 such as classification, retrieval, captioning, or multimodal QA.
• Prepare datasets and apply PEFT strategies to minimize resource consumption.
• Evaluate and deploy customized VLMs within production environments.

This instructor-led live training in Athens (available online or onsite) is designed for intermediate machine learning engineers and AI compliance professionals seeking to identify, assess, and reduce safety risks and biases in fine-tuned language models.

By the conclusion 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 Athens (online or onsite) targets intermediate NLP engineers and knowledge management teams aiming to fine-tune RAG pipelines to enhance performance in question answering, enterprise search, and summarization use cases.

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

• Grasp the architecture and workflow of RAG systems.
• Fine-tune retriever and generator components for domain-specific data.
• Assess RAG performance and implement enhancements using PEFT techniques.
• Deploy optimized RAG systems for internal or production environments.

This live, instructor-led training in Athens (available online or onsite) is designed for intermediate-level ML practitioners and AI developers looking 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.

This instructor-led, live training in Athens (online or in-person) is designed for intermediate-level data scientists and AI engineers seeking to fine-tune large language models more cost-effectively and efficiently, utilizing methods such as LoRA, Adapter Tuning, and Prefix Tuning.

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

• Understanding the theory underlying parameter-efficient fine-tuning approaches.
• Implementing LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Evaluating the performance and cost trade-offs of PEFT methods compared to full fine-tuning.
• Deploying and scaling fine-tuned LLMs with lower compute and storage requirements.

This instructor-led, live training in Athens (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 live, instructor-led training in Athens, conducted either online or on-site, is designed for advanced machine learning engineers and AI researchers seeking to apply RLHF to optimize large AI models for superior performance, safety, and alignment.

Upon completion, participants will be able to:

• Comprehend the theoretical foundations of RLHF and its significance in modern AI development.
• Create reward models based on human feedback to steer reinforcement learning processes.
• Refine large language models using RLHF methods to align outputs with human preferences.
• Apply best practices for scaling RLHF workflows in production-grade AI systems.

This live, instructor-led training in Athens (available online or on-site) is tailored for intermediate-level professionals aiming to acquire practical skills in customizing AI models for critical financial duties.

By the end of this training, participants will be capable of:

• Comprehending the fundamentals of fine-tuning for finance applications.
• Applying pre-trained models to specific tasks within the financial domain.
• Employing techniques for fraud detection, risk assessment, and generating financial advice.
• Ensuring adherence to financial regulations such as GDPR and SOX.
• Embedding data security and ethical AI practices into financial applications.

This instructor-led, live training in Athens (online or onsite) is designed for advanced professionals who wish to enhance their skills in diagnosing and resolving fine-tuning challenges for machine learning models.

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

• Identify issues such as overfitting, underfitting, and data imbalance.
• Deploy strategies to improve model convergence.
• Optimise fine-tuning pipelines for enhanced performance.
• Debug training processes using practical tools and techniques.

This instructor-led, live training in Athens (online or onsite) is aimed at advanced 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 session (online or onsite) is designed for intermediate-level professionals who aim to leverage prompt engineering and few-shot learning to optimize LLM performance for real-world applications.

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

• Understand the fundamental 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 Athens (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 Athens (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 instructor-led, live training in Athens (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 Athens (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 training in Athens 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.

Delivered by an instructor via live session (available online or onsite), this training is tailored for machine learning professionals ranging from beginner to intermediate levels who aim to understand and implement transfer learning techniques to boost efficiency and performance in AI initiatives.

By the conclusion of this training, participants will be capable of:

• Grasping the fundamental concepts and advantages of transfer learning.
• Examining popular pre-trained models and their applications.
• Carrying out fine-tuning of pre-trained models for specialized tasks.
• Applying transfer learning to resolve practical issues in NLP and computer vision.

This instructor-led, live training session in Athens (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 Athens (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 in Athens (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.