Fine-Tuning Training in Prague

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

Upon completing this training, participants will be able to:

• Fine-tune computer vision and sensor fusion models for surveillance and targeting applications.
• Adapt autonomous AI systems to dynamic environments and mission requirements.
• Deploy robust validation and fail-safe mechanisms within model pipelines.
• Align model performance with defense-specific compliance, safety, and security standards.

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

Upon completing this training, participants will be able to:

• Prepare and clean legal documents for fine-tuning NLP models.
• Apply fine-tuning strategies to enhance 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 Prague (online or onsite) targets 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.

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 in medical contexts.
• Address privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

This instructor-led, live training in Prague (online or on-site) is designed for advanced data scientists and AI engineers in the financial sector who want to fine-tune models for applications such as credit scoring, fraud detection, and risk modeling using domain-specific financial data.

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

• Fine-tune AI models on financial datasets to improve fraud and risk prediction.
• Apply techniques such as transfer learning, LoRA, and regularization to boost model efficiency.
• Incorporate financial compliance requirements into the AI modeling workflow.
• Deploy fine-tuned models for production use within financial services platforms.

This instructor-led, live training in Prague (online or onsite) is designed for advanced-level AI maintenance engineers and MLOps professionals who want 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 conducted in Prague (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists who aim to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

Upon completing this training, participants will be capable of:

• Choosing and adapting pre-trained models appropriate for edge deployment.
• Utilizing quantization, pruning, and other compression methods to minimize model size and latency.
• Fine-tuning models via transfer learning to achieve performance tailored to specific tasks.
• Deploying optimized models onto actual edge hardware platforms.

This instructor-led, live training in Prague (online or onsite) is designed for advanced-level computer vision engineers and AI developers who want to fine-tune VLMs, such as CLIP and Flamingo, to improve their performance on industry-specific visual-text tasks.

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

• Comprehend the architecture and pretraining methodologies of vision-language models.
• Fine-tune VLMs for tasks including classification, retrieval, captioning, or multimodal question answering.
• Prepare datasets and apply PEFT strategies to minimize resource consumption.
• Evaluate and deploy customized VLMs within production environments.

This instructor-led live training in Prague, offered online or onsite, is tailored for intermediate-level ML engineers and AI compliance professionals looking to identify, assess, 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 Prague (online or onsite) targets intermediate-level 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:

• 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 Prague (online or onsite) is aimed at intermediate-level ML practitioners and AI developers who wish 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 live, instructor-led training in Prague, available either online or onsite, is designed for intermediate data scientists and AI engineers seeking to fine-tune large language models with improved affordability and efficiency through methods like LoRA, Adapter Tuning, and Prefix Tuning.

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

• Comprehend the theoretical underpinnings of parameter-efficient fine-tuning strategies.
• Apply LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Evaluate and compare the trade-offs regarding performance and cost between PEFT methods and full fine-tuning.
• Deploy and scale fine-tuned LLMs with minimized compute and storage overhead.

This instructor-led, live training in Prague (online or onsite) targets machine learning engineers, AI developers, and data scientists with intermediate to advanced proficiency levels who aim to master the efficient refinement of large models for specific tasks and custom modifications using QLoRA.

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

• Comprehending the theoretical basis of QLoRA and quantization strategies for LLMs.
• Applying QLoRA to refine large language models for domain-specific use cases.
• Enhancing refinement performance on constrained computational hardware via quantization.
• Efficiently deploying and evaluating refined models within real-world contexts.

This live, instructor-led training in Prague (offered online or on-site) targets advanced machine learning engineers and AI researchers aiming to apply RLHF to optimize large AI models for superior performance, safety, and alignment.

By the conclusion of this training, participants will have the ability to:

• Comprehend the theoretical basis of RLHF and its significance in modern AI evolution.
• Create reward models based on human input to steer reinforcement learning.
• Adjust large language models through RLHF techniques to match human expectations.
• Utilize best practices to scale RLHF operations for enterprise-grade AI systems.

This instructor-led, live training session in Prague (conducted online or onsite) targets intermediate-level professionals aiming to develop practical skills in customizing AI models for key financial responsibilities.

By the end of the training, participants will have the capability to:

• Understand the foundational aspects of fine-tuning for financial applications.
• Leverage pre-trained models to handle domain-specific financial tasks.
• Apply techniques for fraud detection, risk assessment, and the generation of financial advice.
• Ensure adherence to financial regulations such as GDPR and SOX.
• Implement data security and ethical AI practices within financial applications.

This instructor-led, live training in Prague (online or onsite) targets advanced-level professionals who wish to refine their skills in diagnosing and solving fine-tuning challenges for machine learning models.

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

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

This instructor-led, live training in Prague (online or onsite) is aimed at advanced-level 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 in Prague (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 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 training in Prague (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 Prague (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 Prague (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 Prague (online or onsite) is designed for advanced-level machine learning professionals who aim to master state-of-the-art transfer learning techniques and apply them to complex, real-world challenges.

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

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

This instructor-led training in Prague focuses on advanced fine-tuning and prompt management within Vertex AI. It assists intermediate and advanced practitioners in optimizing Gemini models, establishing robust evaluation workflows, and deploying dependable generative AI solutions for real-world enterprise scenarios.

This instructor-led, live training in Prague (online or onsite) is aimed at beginner-level to intermediate-level 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:

• Grasp 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 Prague (online or onsite) is designed for intermediate-level developers and AI practitioners seeking to implement fine-tuning strategies for large models without requiring extensive computational resources.

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

• Comprehend the core principles of Low-Rank Adaptation (LoRA).
• Implement LoRA for efficient fine-tuning of large models.
• Optimize fine-tuning workflows for resource-constrained environments.
• Evaluate and deploy LoRA-tuned models for practical use cases.

This instructor-led, live training in Prague (online or onsite) is designed for intermediate to advanced professionals who wish 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 Prague (online or onsite) is designed for intermediate-level professionals looking 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 to improve model performance.
• Evaluate and deploy fine-tuned models in real-world scenarios.