Domain-Specific Fine-Tuning for Finance Training Course

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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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.

This instructor-led, live training in Uzbekistan (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 guided, live training in Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 instructor-led, live training in Uzbekistan (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 instructor-led, live training in Uzbekistan (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.