Fine-Tuning Training in Tashkent

This instructor-led, live training in Tashkent (online or onsite) is aimed at 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 meeting stringent security and reliability standards.

By the end 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 Tashkent (online or onsite) is designed for intermediate-level legal technology engineers and AI developers who want to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research within 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 in legal tasks.
• Deploy models to support contract review, classification, and research.
• Ensure compliance, auditability, and traceability of AI outputs in legal contexts.

This guided, live training in Tashkent (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists aiming to optimize models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

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

• Optimize AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Utilize transfer learning, domain adaptation, and model compression in medical scenarios.
• Handle privacy, bias, and regulatory compliance aspects in model development.
• Deploy and monitor optimized models in real-world healthcare environments.

This instructor-led, live training in Tashkent (online or in-person) targets experienced data scientists and AI engineers in the financial industry who want to refine models for tasks like credit scoring, fraud detection, and risk modeling using specialized financial data.

Upon completing this training, participants will be capable of:

• Refining AI models on financial data to improve predictions for fraud and risk.
• Utilizing methods such as transfer learning, LoRA, and regularization to boost model performance.
• Incorporating financial compliance requirements into the AI modeling process.
• Deploying refined models for use in financial service platforms.

This instructor-led, live training in Tashkent (online or onsite) targets advanced-level AI maintenance engineers and MLOps professionals seeking 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 Tashkent (online or on-site) is designed for intermediate-level embedded AI developers and edge computing specialists seeking to fine-tune and optimise lightweight AI models for deployment on resource-constrained devices.

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

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantisation, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning to achieve task-specific performance.
• Deploy optimised models on real-world edge hardware platforms.

This instructor-led, live training in Tashkent (online or onsite) targets advanced computer vision engineers and AI developers aiming to fine-tune VLMs such as CLIP and Flamingo to enhance 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 like classification, retrieval, captioning, or multimodal question answering.
• Prepare datasets and implement PEFT strategies to minimize resource consumption.
• Evaluate and deploy customized VLMs within production environments.

This instructor-led live training in Tashkent (available online or onsite) is tailored for intermediate-level ML engineers and AI compliance professionals seeking to identify, assess, and mitigate safety risks and biases in fine-tuned language models.

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

• Comprehend the ethical and regulatory frameworks governing safe AI systems.
• Recognize and evaluate prevalent forms of bias present in fine-tuned models.
• Implement bias mitigation strategies during and after the training phase.
• Develop and audit models with a focus on safety, transparency, and fairness.

This instructor-led, live training in Tashkent (online or on-site) 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 summarisation 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 optimised RAG systems for internal or production use.

This instructor-led, live training in Tashkent (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 instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level data scientists and AI engineers who want to fine-tune large language models in a more cost-effective and efficient manner using techniques such as LoRA, Adapter Tuning, and Prefix Tuning.

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

• Grasp the theoretical foundations of parameter-efficient fine-tuning approaches.
• Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Evaluate the performance and cost trade-offs between PEFT methods and full fine-tuning.
• Deploy and scale fine-tuned LLMs while minimizing compute and storage demands.

This instructor-led, live training in Tashkent (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 guided, live training in Tashkent (online or on-site) is designed for experienced machine learning engineers and AI researchers who want to apply RLHF to fine-tune large AI models for improved performance, safety, and alignment.

Upon completing this training, participants will be able to:

• Grasp the theoretical underpinnings of RLHF and its critical role in contemporary AI development.
• Develop reward models based on human feedback to direct reinforcement learning processes.
• Fine-tune large language models using RLHF methods to ensure outputs align with human preferences.
• Apply industry best practices for scaling RLHF workflows in production-grade AI systems.

This instructor-led live training in Tashkent (online or onsite) targets intermediate-level professionals eager to develop practical skills in customizing AI models for critical financial tasks.

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

• Grasp the core principles of fine-tuning AI for financial applications.
• Utilize pre-trained models for specialized tasks within the finance sector.
• Apply techniques for fraud detection, risk assessment, and generating financial advice.
• Ensure adherence to financial regulations, including GDPR and SOX.
• Implement robust data security and ethical AI practices in financial software.

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

By the conclusion 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 live, instructor-led training in Tashkent (online or onsite) is designed for advanced professionals aiming 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 Tashkent (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of 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 Tashkent (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 Tashkent (online or onsite) is designed for advanced AI researchers, machine learning engineers, and developers who wish to customize DeepSeek LLM models to develop specialized AI applications for specific industries, domains, or business needs.

Upon completing this training, participants will be able to:

• Grasp the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Prepare and preprocess datasets suitable for fine-tuning.
• Execute fine-tuning processes for DeepSeek LLM to address domain-specific challenges.
• Optimize and efficiently deploy fine-tuned models.

This instructor-led, live training in Tashkent (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 Tashkent (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.

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 aiming to improve the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.

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

• Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implement prompt management workflows that include versioning and testing.
• Leverage 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 focused on enterprise model optimization.

Course Customization Options

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

This instructor-led, live training in Tashkent (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:

• 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 Tashkent (available online or on-site) is designed for 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 core principles of Low-Rank Adaptation (LoRA).
• Implement LoRA for efficient fine-tuning of large models.
• Optimize fine-tuning processes for environments with limited resources.
• Evaluate and deploy LoRA-tuned models in practical applications.

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

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

• Grasp the core principles of fine-tuning and its real-world applications.
• Prepare datasets effectively for fine-tuning pre-trained models.
• Fine-tune large language models (LLMs) for NLP tasks.
• Enhance model performance and resolve common challenges.

This instructor-led, live training in Tashkent (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.