Domain-Specific Fine-Tuning for Finance Training Course

Vertex AI offers sophisticated tools for fine-tuning large language 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 both online and onsite, is designed for intermediate to advanced practitioners who aim to improve the performance and reliability of generative AI applications by leveraging 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.
• Leverage evaluation libraries to benchmark and optimize AI performance.
• Deploy and monitor improved models in production environments.

Format of the Course

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

Course Customization Options

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

Delivered by an instructor in a live format (available online or onsite), this programme is designed for seasoned machine learning professionals aiming to master state-of-the-art transfer learning techniques and apply them to intricate real-world scenarios.

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

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

This instructor-led, live training in Kenya (online or onsite) targets advanced AI maintenance engineers and MLOps professionals who aim to establish resilient ongoing learning pipelines and effective revision strategies for deployed, fine-tuned models.

Upon completing this training, participants will be capable of:

• Designing and executing ongoing learning workflows for deployed models.
• Reducing catastrophic forgetting through appropriate training and memory management techniques.
• Automating monitoring and update triggers in response to model drift or data changes.
• Incorporating model revision strategies into existing CI/CD and MLOps pipelines.

This instructor-led, live training in Kenya (online or on-site) is designed for advanced professionals seeking to deploy fine-tuned models reliably and efficiently.

Upon completion 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 Kenya (online or onsite) is designed for intermediate to advanced-level professionals seeking 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 model refinement and its real-world applications.
• Prepare datasets effectively for refining pre-trained models.
• Refine Large Language Models (LLMs) for Natural Language Processing (NLP) tasks.
• Optimize model performance and navigate common challenges.

This instructor-led live training in Kenya (online or onsite) is designed for intermediate developers and AI practitioners aiming to implement fine-tuning strategies for large models without the need for extensive computational resources.

Upon completing this training, participants will be equipped to:

• Comprehend the foundational principles of Low-Rank Adaptation (LoRA).
• Apply LoRA for the efficient fine-tuning of large models.
• Enhance fine-tuning processes for resource-constrained settings.
• Assess and deploy LoRA-optimized models for real-world applications.

This instructor-led, live training in Kenya (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 Kenya (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.
• 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.

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

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

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

This instructor-led, live training in Kenya (online or onsite) is designed for medical AI developers and data scientists at intermediate to advanced levels who wish 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 EMRs, imaging, and time-series data.
• Implement transfer learning, domain adaptation, and model compression within medical contexts.
• Manage privacy concerns, bias, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in real-world healthcare settings.

This instructor-led, live training session, available online or onsite, is designed for advanced AI researchers, machine learning engineers, and developers aiming to fine-tune DeepSeek LLM models. The objective is to create specialized AI applications tailored to specific industries, domains, or business needs.

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

• Comprehend the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Prepare and preprocess datasets for the fine-tuning process.
• Fine-tune DeepSeek LLM for applications targeted at specific domains.
• Optimize and deploy fine-tuned models with efficiency.

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

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

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

This instructor-led, live training in Kenya (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 guided, live instruction Kenya (available online or onsite) is tailored for intermediate to advanced-level machine learning engineers, AI developers, and data scientists who seek to learn how to utilize QLoRA for the efficient fine-tuning of large models for specific tasks and customizations.

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

• Comprehend the theory underpinning QLoRA and quantization techniques for LLMs.
• Execute QLoRA for refining large language models in domain-specific contexts.
• Refine 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 Kenya (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists seeking to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

By the conclusion 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.