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 integrated libraries and services.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners seeking 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, including versioning and testing.
• Leverage evaluation libraries to benchmark and optimise AI performance.
• Deploy and monitor enhanced models in production environments.

Course Format

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

Course Customisation Options

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

This instructor-led, live training in Botswana (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 Botswana (online or at your premises) targets experienced AI maintenance engineers and MLOps specialists who want to build strong ongoing learning pipelines and effective refresh strategies for models that have been deployed and fine-tuned.

Upon completing this training, participants will be capable of:

• Creating and executing ongoing learning workflows for deployed models.
• Reducing catastrophic forgetting via proper training and memory management.
• Automating monitoring and refresh triggers based on model drift or data shifts.
• Incorporating model refresh strategies into existing CI/CD and MLOps pipelines.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) is designed for intermediate to advanced professionals seeking to customise pre-trained models for distinct tasks and datasets.

Upon completion of this training, participants will be capable of:

• Grasping the fundamental principles of refinement and its applications.
• Preparing datasets for the refinement of pre-trained models.
• Refining Large Language Models (LLMs) for Natural Language Processing (NLP) tasks.
• Optimising model performance and resolving common challenges.

This instructor-led, live training in Botswana (online or onsite) targets 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 principles of Low-Rank Adaptation (LoRA).
• Implement LoRA for efficient fine-tuning of large models.
• Optimize fine-tuning for resource-constrained environments.
• Evaluate and deploy LoRA-tuned models for practical applications.

This instructor-led, live training in Botswana (online or onsite) is designed for advanced-level professionals aiming to master multimodal model fine-tuning for innovative AI solutions.

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

• Grasp the architecture of multimodal models such as CLIP and Flamingo.
• Effectively prepare and preprocess multimodal datasets.
• Fine-tune multimodal models for specific tasks.
• Optimise models for real-world applications and performance.

This instructor-led, live training in Botswana (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 Botswana (online or in-person) is designed for advanced data scientists and AI engineers within the financial sector who wish to adapt models for applications such as credit scoring, fraud detection, and risk modelling using finance-specific data.

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

• Adapt AI models using financial datasets to enhance the prediction of fraud and risk.
• Utilise techniques like transfer learning, LoRA, and regularisation to boost model efficiency.
• Incorporate financial regulatory compliance into the AI modelling process.
• Deploy fine-tuned models for operational use within financial services platforms.

This instructor-led, live training in Botswana (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, and time-series data.
• Apply transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Address privacy, bias, and regulatory compliance issues in model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

This instructor-led, live training in Botswana (online or onsite) targets senior-level AI researchers, machine learning engineers, and developers keen to fine-tune DeepSeek LLM models for crafting specialised AI applications suited to specific industries, domains, or operational needs.

Upon completion of this training, participants will be capable of:

• Grasping the architecture and functionalities of DeepSeek models, notably DeepSeek-R1 and DeepSeek-V3.
• Preparing datasets and performing data preprocessing suitable for fine-tuning.
• Executing fine-tuning of DeepSeek LLM for domain-specific applications.
• Optimising and efficiently deploying fine-tuned models.

This instructor-led, live training in Botswana (online or onsite) targets advanced-level defense AI engineers and military technology developers who wish to refine deep learning models for deployment in autonomous vehicles, drones, and surveillance systems, ensuring they meet rigorous 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 roles.
• Adapt autonomous AI systems to fluctuating environments and mission requirements.
• Integrate sturdy validation and fail-safe mechanisms into model workflows.
• Ensure compliance with defense-specific safety, security, and regulatory standards.

This instructor-led, live training in Botswana (online or on-site) targets 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 within legal service environments.

Upon completion 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 Botswana (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 Botswana (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists who aim to fine-tune and optimise lightweight AI models for deployment on devices with limited resources.

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

• Choose and adapt pre-trained models appropriate for edge deployment.
• Apply quantization, pruning, and other compression methods to decrease model size and latency.
• Fine-tune models using transfer learning to achieve task-specific performance.
• Deploy optimized models on actual edge hardware platforms.