Fine-Tuning Training in Belgrade

This instructor-led, live training in Belgrade (online or onsite) is designed for advanced-level defense AI engineers and military technology developers who wish to fine-tune deep learning models for use in autonomous vehicles, drones, and surveillance systems while adhering to strict security and reliability standards.

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

• Fine-tune computer vision and sensor fusion models for surveillance and targeting tasks.
• Adapt autonomous AI systems to changing environments and mission profiles.
• Implement robust validation and fail-safe mechanisms in model pipelines.
• Ensure alignment with defense-specific compliance, safety, and security standards.

This instructor-led, live training in Belgrade (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 Belgrade (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 Belgrade (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 Belgrade (online or onsite) is aimed at advanced-level 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 Belgrade (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists aiming to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

Upon completion of 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 minimize model size and reduce latency.
• Fine-tuning models through transfer learning to achieve task-specific performance.
• Deploying optimized models on actual edge hardware platforms.

This instructor-led, live training in Belgrade (online or onsite) is aimed at advanced-level 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.

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

• Understand the architecture and pretraining methods of vision-language models.
• Fine-tune 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, offered in Belgrade (online or onsite), targets intermediate-level ML engineers and AI compliance professionals seeking to identify, evaluate, and minimize safety risks and biases in fine-tuned language models.

By the end of the 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 Belgrade (online or onsite) targets intermediate-level NLP engineers and knowledge management teams looking to fine-tune RAG pipelines to boost 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 Belgrade (online or onsite) is designed for intermediate-level ML practitioners and AI developers who want 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:

• Grasp the ecosystem and distinctions among 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 Belgrade (available online or onsite) is intended for intermediate-level data scientists and AI engineers who seek to fine-tune large language models more affordably and efficiently using techniques like LoRA, Adapter Tuning, and Prefix Tuning.

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

• Understand the theoretical concepts behind parameter-efficient fine-tuning approaches.
• Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Analyze the performance and cost trade-offs of PEFT methods compared to full fine-tuning.
• Deploy and scale fine-tuned LLMs with minimized compute and storage requirements.

This instructor-led, live training in Belgrade (online or onsite) targets intermediate to advanced machine learning engineers, AI developers, and data scientists seeking to learn how to utilize QLoRA for the efficient fine-tuning of large models tailored to specific tasks.

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

• Understanding the theory behind QLoRA and quantization techniques for LLMs.
• Implementing QLoRA to fine-tune large language models for domain-specific applications.
• Optimizing fine-tuning performance on limited computational resources using quantization.
• Deploying and evaluating fine-tuned models efficiently in real-world applications.

This instructor-led, live training in Belgrade (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.

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

• Gaining insight into the theoretical underpinnings of RLHF and its critical role in contemporary AI development.
• Developing reward models driven by human feedback to steer reinforcement learning procedures.
• Fine-tuning large language models using RLHF methods to ensure outputs align with human preferences.
• Applying industry best practices for scaling RLHF workflows in production-grade AI systems.

This instructor-led, live training in Belgrade (online or onsite) is designed for intermediate-level professionals who want to develop practical skills in customizing AI models for critical financial tasks.

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

• Comprehend the core principles of fine-tuning for financial applications.
• Utilize pre-trained models for finance-specific tasks.
• Apply methods for detecting fraud, assessing risk, and generating financial advice.
• Ensure adherence to financial regulations, including GDPR and SOX.
• Execute data security measures and ethical AI standards within financial applications.

This live, instructor-led training in Belgrade (online or onsite) is designed for advanced professionals who aim to sharpen their skills in diagnosing and solving fine-tuning challenges for machine learning models.

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

• Identify problems such as overfitting, underfitting, and data imbalance.
• Apply strategies to enhance model convergence.
• Enhance fine-tuning pipelines for superior performance.
• Troubleshoot training processes using practical tools and techniques.

This instructor-led, live training in Belgrade (online or onsite) targets 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:

• Grasp the challenges associated with fine-tuning large models.
• Implement distributed training techniques for large models.
• Utilize model quantization and pruning for improved efficiency.
• Optimize hardware usage for fine-tuning tasks.
• Effectively deploy fine-tuned models in production environments.

This instructor-led, live training in Belgrade (online or onsite) is designed for intermediate-level professionals who want to leverage 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 session (remote or in-person) is designed for advanced professionals seeking to master multimodal model fine-tuning for innovative AI solutions.

Upon completion, participants will be capable of:

• Gaining insight into the structure of models like CLIP and Flamingo.
• Effectively preparing and preprocessing complex multimodal data.
• Customizing multimodal models for distinct objectives.
• Enhancing model performance for practical, real-world use.

This instructor-led, live training in Belgrade (online or onsite) is designed for advanced AI researchers, machine learning engineers, and developers aiming to fine-tune DeepSeek LLM models to create specialized AI applications tailored to specific industries, domains, or business requirements.

Upon completing this training, participants will be able to:

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

This instructor-led, live training in Belgrade (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 Belgrade (online or on-site) targets 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.

Vertex AI offers sophisticated tools for fine-tuning large 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) targets 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 able to:

• Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implement prompt management workflows, including versioning and testing.
• Utilize evaluation libraries to benchmark and optimize AI performance.
• Deploy and monitor improved models in production environments.

Course Format

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

Course Customization Options

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

This instructor-led, live session in Belgrade (available online or on-site) is intended for beginner to intermediate machine learning professionals who aim to understand and apply transfer learning techniques to enhance efficiency and performance in AI projects.

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

• Understand the core principles and advantages of transfer learning.
• Explore popular pre-trained models and their respective applications.
• Execute fine-tuning of pre-trained models for bespoke tasks.
• Apply transfer learning solutions to real-world issues in NLP and computer vision.

This instructor-led, live training in Belgrade (online or onsite) targets intermediate-level developers and AI practitioners aiming 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:

• Grasp the principles of Low-Rank Adaptation (LoRA).
• Utilize 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 Belgrade (online or onsite) is aimed at intermediate-level 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:

• 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 Belgrade (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:

• Grasp the core concepts behind fine-tuning for NLP applications.
• Apply fine-tuning techniques to pre-trained models like GPT, BERT, and T5 for targeted NLP use cases.
• Adjust hyperparameters to boost model effectiveness.
• Assess and implement fine-tuned models in practical, real-world settings.