Model Refinement & Adaptation Training in Ankara

This instructor-led, live training in Ankara (online or onsite) targets advanced-level defense AI engineers and military technology developers who seek to refine deep learning models for use in autonomous vehicles, drones, and surveillance systems while meeting stringent security and reliability standards.

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

• Refine computer vision and sensor fusion models for surveillance and targeting operations.
• Adapt autonomous AI systems to dynamic environments and varying mission profiles.
• Implement rigorous validation and fail-safe mechanisms within model pipelines.
• Ensure compliance with defense-specific regulations, safety, and security standards.

This instructor-led, live training in Ankara (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 Ankara (online or on-site) targets intermediate to advanced medical AI developers and data scientists who want to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

Upon completing this training, participants will be able to:

• Fine-tune AI models using healthcare datasets such as electronic medical records (EMRs), imaging data, and time-series information.
• Implement transfer learning, domain adaptation, and model compression strategies within medical applications.
• Tackle issues related to privacy, algorithmic bias, and regulatory compliance during model development.
• Deploy and oversee fine-tuned models in practical healthcare settings.

This instructor-led, live training in Ankara (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 Ankara (online or onsite) targets advanced-level AI maintenance engineers and MLOps professionals who aim to implement robust continual learning pipelines and effective update strategies for deployed, fine-tuned models.

Upon completing this training, participants will be able to:

• Design and implement continual learning workflows for deployed models.
• Reduce catastrophic forgetting through proper training techniques 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 Ankara (online or onsite) is intended 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.

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 Ankara (online or onsite) targets advanced-level computer vision engineers and AI developers seeking to refine VLMs such as CLIP and Flamingo to improve performance on industry-specific visual-text tasks.

Upon completing this training, participants will be able to:

• Grasp the architecture and pretraining techniques of vision-language models.
• Refine VLMs for tasks including classification, retrieval, captioning, or multimodal question answering.
• Prepare datasets and implement PEFT strategies to optimize resource consumption.
• Evaluate and deploy customized VLMs within production environments.

This instructor-led live training in Ankara (available online or onsite) targets 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 Ankara (online or onsite) targets intermediate-level NLP engineers and knowledge management teams who wish to fine-tune RAG pipelines to improve 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 Ankara (online or onsite) is designed for intermediate-level machine learning 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:

• Comprehend the ecosystem and key distinctions among various open-source LLMs.
• Prepare datasets and configure fine-tuning parameters for models like LLaMA, Mistral, and Qwen.
• Implement fine-tuning pipelines utilizing Hugging Face Transformers and PEFT.
• Evaluate, save, and deploy fine-tuned models within secure environments.

This live, instructor-led program in Ankara—delivered online or onsite—is tailored for intermediate data scientists and AI engineers looking to optimize LLM adaptation through cost-effective methods like LoRA, Adapter Tuning, and Prefix Tuning.

Participants will gain the ability to:

• Comprehend the theoretical foundations of parameter-efficient fine-tuning.
• Execute LoRA, Adapter Tuning, and Prefix Tuning via Hugging Face PEFT.
• Assess the balance between performance and cost when comparing PEFT to full fine-tuning.
• Manage the deployment and scaling of fine-tuned LLMs with optimized compute and storage usage.

This instructor-led, live training in Ankara (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:

• Grasp the theoretical underpinnings of QLoRA and quantization techniques for LLMs.
• Apply QLoRA to fine-tune large language models for domain-specific applications.
• Enhance fine-tuning performance on constrained computational resources through quantization.
• Efficiently deploy and evaluate fine-tuned models in real-world scenarios.

This instructor-led, live training session in Ankara (online or onsite) targets advanced-level 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 conclusion of this training, participants will be able to:

• Comprehend the theoretical foundations of RLHF and understand its necessity in modern AI development.
• Construct 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 Ankara (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:

• Understand the fundamentals of fine-tuning for finance applications.
• Leverage pre-trained models for domain-specific tasks in finance.
• Apply techniques for fraud detection, risk assessment, and financial advice generation.
• Ensure compliance with financial regulations such as GDPR and SOX.
• Implement data security and ethical AI practices in financial applications.

This instructor-led, live training in Ankara (online or onsite) is aimed at 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:

• Diagnose issues like 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 Ankara (online or onsite) is designed for advanced professionals looking to master techniques for optimizing large models for cost-effective fine-tuning in real-world scenarios.

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

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

This instructor-led, live training in Ankara (online or onsite) targets intermediate-level professionals who want to utilize 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 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 Ankara (online or onsite) is tailored for advanced professionals who want 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 Ankara (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 Ankara (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 Ankara (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 Ankara (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 Ankara (online or onsite) is designed for intermediate-level developers and AI practitioners seeking to implement fine-tuning strategies for large models without the need for substantial computational resources.

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

• Understand the fundamental principles of Low-Rank Adaptation (LoRA).
• Implement LoRA for the efficient fine-tuning of large models.
• Optimize fine-tuning workflows for resource-constrained environments.
• Evaluate and deploy LoRA-tuned models for practical, real-world applications.

This instructor-led, live training in Ankara (online or onsite) is designed for intermediate to advanced professionals looking to customize pre-trained models for specific tasks and datasets.

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

• Grasp the principles of refining and its applications.
• Prepare datasets for refining pre-trained models.
• Refine large language models (LLMs) for NLP tasks.
• Optimize model performance and address common challenges.

This instructor-led, live training in Ankara (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 fundamentals of fine-tuning for NLP tasks.
• Apply fine-tuning to pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
• Tune hyperparameters to boost model performance.
• Assess and deploy fine-tuned models in real-world scenarios.