Fine-Tuning Training in Plovdiv

This instructor-led, live training in Plovdiv (online or onsite) is designed for advanced defense AI engineers and military technology developers. The program focuses on fine-tuning deep learning models for autonomous vehicles, drones, and surveillance systems, ensuring adherence to rigorous security and reliability standards.

Upon completing this training, participants will be able to:

• Optimize computer vision and sensor fusion models for surveillance and targeting operations.
• Adjust autonomous AI systems to adapt to dynamic environments and mission profiles.
• Deploy robust validation and fail-safe mechanisms within model pipelines.
• Ensure compliance with defense-specific safety, security, and regulatory standards.

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

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

• Preparing and cleaning legal documents for NLP model fine-tuning.
• Implementing fine-tuning strategies to enhance model accuracy for legal tasks.
• Deploying models to support contract review, classification, and research.
• Ensuring compliance, auditability, and traceability of AI outputs in legal settings.

This instructor-led, live training in Plovdiv (online or in-person) targets intermediate to advanced medical AI developers and data scientists aiming to refine models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

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

• Refining AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Implementing transfer learning, domain adaptation, and model compression within medical contexts.
• Tackling issues of privacy, bias, and regulatory compliance during model development.
• Deploying and monitoring refined models in real-world healthcare settings.

This instructor-led, live training in Plovdiv (online or in-person) 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 Plovdiv (online or onsite) is designed for advanced AI maintenance engineers and MLOps professionals who wish to implement robust continuous 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 continuous learning workflows for deployed models.
• Prevent catastrophic forgetting through appropriate 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 session in Plovdiv (online or in-person) targets intermediate embedded AI developers and edge computing experts looking to refine and optimize compact AI models for deployment on devices with limited resources.

Upon completing this training, participants will be capable of:

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

This instructor-led, live training in Plovdiv (online or in-person) is aimed at advanced-level computer vision engineers and AI developers who wish to refine VLMs such as CLIP and Flamingo to improve 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.
• Refine 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 Plovdiv (online or onsite) is tailored for intermediate-level ML engineers and AI compliance professionals seeking to identify, evaluate, 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 Plovdiv (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 Plovdiv (online or onsite) is intended 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 Plovdiv (offered online or onsite) is designed for intermediate-level data scientists and AI engineers seeking to fine-tune large language models more affordably and efficiently using techniques such as LoRA, Adapter Tuning, and Prefix Tuning.

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

• Grasp the theoretical foundations underlying parameter-efficient fine-tuning approaches.
• Apply LoRA, Adapter Tuning, and Prefix Tuning using the Hugging Face PEFT library.
• Analyze the performance and cost trade-offs between PEFT methods and traditional full fine-tuning.
• Deploy and scale fine-tuned LLMs while minimizing computational and storage demands.

This live, instructor-led training Plovdiv (online or onsite) targets intermediate to advanced machine learning engineers, AI developers, and data scientists eager to master the use of QLoRA for efficiently fine-tuning large models for specific tasks and customizations.

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

• Comprehend the theory behind QLoRA and quantization techniques for LLMs.
• Implement QLoRA in the fine-tuning of 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 Plovdiv (online or onsite) is designed for 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 importance 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 Plovdiv (online or onsite) is designed for intermediate-level professionals aiming to develop practical skills in customizing AI models for critical financial tasks.

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

• Grasping the core principles of fine-tuning for financial applications.
• Utilizing pre-trained models for finance-specific tasks.
• Applying methods for fraud detection, risk assessment, and generating financial advice.
• Ensuring adherence to financial regulations such as GDPR and SOX.
• Executing data security measures and ethical AI standards in financial solutions.

This instructor-led, live training in Plovdiv (online or on-site) is intended for advanced professionals who want to sharpen their skills in diagnosing and solving fine-tuning challenges for machine learning models.

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

• Diagnose issues such as 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 Plovdiv (online or onsite) is designed for advanced professionals seeking 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 in Plovdiv (online or onsite) is designed for intermediate-level professionals seeking to utilize prompt engineering and few-shot learning to optimize LLM performance for real-world applications.

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

• Understanding the core principles of prompt engineering and few-shot learning.
• Designing effective prompts for various NLP tasks.
• Leveraging few-shot techniques to adapt LLMs with minimal data.
• Optimizing LLM performance for practical applications.

This instructor-led, live training in Plovdiv (online or onsite) is designed for advanced professionals aiming to master the fine-tuning of multimodal models 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 Plovdiv (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 Plovdiv (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 Plovdiv (online or onsite) is designed for advanced machine learning professionals aiming to master state-of-the-art transfer learning techniques and apply them to complex real-world scenarios.

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

• Grasp advanced concepts and methodologies in transfer learning.
• Apply domain-specific adaptation techniques to pre-trained models.
• Utilize continual learning to handle evolving tasks and datasets.
• Master multi-task fine-tuning to improve model performance across various tasks.

Vertex AI offers sophisticated tools for fine-tuning large language models and managing prompts, empowering developers and data teams to enhance model accuracy, streamline iteration workflows, and ensure rigorous evaluation through built-in libraries and services.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners seeking to improve the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.

Upon completing this training, participants will be capable of:

• Applying supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implementing prompt management workflows that include versioning and testing.
• Leveraging evaluation libraries to benchmark and optimize AI performance.
• Deploying and monitoring enhanced models in production environments.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on Vertex AI fine-tuning and prompt tools.
• Case studies demonstrating enterprise model optimization.

Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training in Plovdiv (online or onsite) is designed for beginner to intermediate 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 Plovdiv (online or onsite) is aimed at 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 Plovdiv (online or onsite) is designed for intermediate-level to advanced-level professionals who want to customize pre-trained models for particular tasks and datasets.

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

• Grasp the principles of fine-tuning and its various 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 Plovdiv (online or onsite) is designed for intermediate-level professionals seeking to enhance their NLP projects through the effective fine-tuning of pre-trained language models.

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

• Grasp the fundamentals of fine-tuning for NLP tasks.
• Fine-tune pre-trained models, including GPT, BERT, and T5, for specific NLP applications.
• Optimize hyperparameters to enhance model performance.
• Evaluate and deploy fine-tuned models in real-world scenarios.