Domain-Specific Fine-Tuning for Finance Training Course

This instructor-led session in South Africa explores advanced fine-tuning and prompt management within Vertex AI. It supports intermediate and advanced practitioners in optimizing Gemini models, implementing robust evaluation workflows, and deploying dependable generative AI solutions for real-world enterprise scenarios.

This instructor-led, live training in South Africa (online or onsite) targets 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 South Africa (online or onsite) is aimed at advanced-level AI maintenance engineers and MLOps professionals who wish 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 South Africa (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 South Africa (online or onsite) is designed for intermediate to advanced professionals who wish to tailor pre-trained models for specific tasks and datasets.

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

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

This instructor-led, live training in South Africa (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 conclusion of this training, participants will be able to:

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

This instructor-led, live training in South Africa (online or in-person) is designed for advanced professionals who want to master multimodal model refinement for innovative AI solutions.

Upon completing this training, participants will be able to:

• Comprehend the architecture of multimodal models such as CLIP and Flamingo.
• Effectively prepare and preprocess multimodal datasets.
• Refine multimodal models for specific tasks.
• Optimize models for real-world applications and performance.

This instructor-led live training in South Africa (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 South Africa (online or onsite) is aimed at advanced-level data scientists and AI engineers in the financial sector who wish to refine models for applications such as credit scoring, fraud detection, and risk modelling using domain-specific financial data.

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

• Refine AI models on financial datasets for improved fraud and risk prediction.
• Apply techniques such as transfer learning, LoRA, and regularisation to enhance model efficiency.
• Integrate financial compliance considerations into the AI modelling workflow.
• Deploy refined models for production use in financial services platforms.

This instructor-led live training in South Africa (online or onsite) targets intermediate to advanced medical AI developers and data scientists who wish 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 data, and time-series data.
• Apply transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Address 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 in South Africa (online or onsite) is designed for advanced-level AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to create specialised 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.
• Optimise and deploy fine-tuned models efficiently.

This instructor-led, live training in South Africa (online or onsite) is designed for advanced defence AI engineers and military technology developers who wish to fine-tune deep learning models for use in autonomous vehicles, drones, and surveillance systems, whilst meeting stringent security and reliability standards.

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

• Fine-tune computer vision and sensor fusion models for surveillance and targeting operations.
• Adapt autonomous AI systems to dynamic environments and varying mission profiles.
• Implement robust validation and fail-safe mechanisms within model pipelines.
• Ensure strict alignment with defence-specific compliance, safety, and security standards.

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

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

• Prepare and cleanse legal documents for the fine-tuning of 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 South Africa (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 customisations.

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

• Understand the theory behind QLoRA and quantisation techniques for LLMs.
• Implement QLoRA in fine-tuning large language models for domain-specific applications.
• Optimise fine-tuning performance on limited computational resources using quantisation.
• Deploy and evaluate fine-tuned models in real-world applications efficiently.

This instructor-led, live training in South Africa (online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimise 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.