Safety and Bias Mitigation in Fine-Tuned Models Training Course
The issue of safety and bias mitigation in fine-tuned models is becoming increasingly critical as artificial intelligence becomes deeply integrated into decision-making processes across various industries, while regulatory standards continue to evolve.
This instructor-led live training, available either online or onsite, is designed for intermediate-level Machine Learning engineers and AI compliance professionals who aim to identify, evaluate, and minimise safety risks and biases in fine-tuned language models.
Upon completion of this training, participants will be able to:
- Grasp the ethical and regulatory landscape governing safe AI systems.
- Identify and assess common types of bias present in fine-tuned models.
- Implement bias mitigation strategies during and after the training phase.
- Design and audit models with a focus on safety, transparency, and fairness.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- For those seeking a customised training version of this course, please contact us to arrange.
Course Outline
Foundations of Safe and Fair AI
- Key concepts: safety, bias, fairness, transparency.
- Types of bias: dataset, representation, algorithmic.
- Overview of regulatory frameworks (e.g., EU AI Act, GDPR).
Bias in Fine-Tuned Models
- How fine-tuning can introduce or amplify bias.
- Case studies and real-world failures.
- Identifying bias in datasets and model predictions.
Techniques for Bias Mitigation
- Data-level strategies (rebalancing, augmentation).
- In-training strategies (regularization, adversarial debiasing).
- Post-processing strategies (output filtering, calibration).
Model Safety and Robustness
- Detecting unsafe or harmful outputs.
- Adversarial input handling.
- Red teaming and stress testing fine-tuned models.
Auditing and Monitoring AI Systems
- Bias and fairness evaluation metrics (e.g., demographic parity).
- Explainability tools and transparency frameworks.
- Ongoing monitoring and governance practices.
Toolkits and Hands-On Practice
- Using open-source libraries (e.g., Fairlearn, Transformers, CheckList).
- Hands-on: Detecting and mitigating bias in a fine-tuned model.
- Generating safe outputs through prompt design and constraints.
Enterprise Use Cases and Compliance Readiness
- Best practices for integrating safety in LLM workflows.
- Documentation and model cards for compliance.
- Preparing for audits and external reviews.
Summary and Next Steps
Requirements
- A foundational understanding of machine learning models and their training processes.
- Practical experience with fine-tuning techniques and Large Language Models (LLMs).
- Familiarity with Python programming and Natural Language Processing (NLP) concepts.
Audience
- AI compliance teams.
- Machine Learning engineers.
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Safety and Bias Mitigation in Fine-Tuned Models Training Course - Enquiry
Upcoming Courses
Related Courses
Advanced Fine-Tuning & Prompt Management in Vertex AI
14 HoursAdvanced Techniques in Transfer Learning
14 HoursThis 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.
Continual Learning and Model Update Strategies for Fine-Tuned Models
14 HoursThis 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.
Deploying Fine-Tuned Models in Production
21 HoursThis 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.
Domain-Specific Fine-Tuning for Finance
21 HoursThis instructor-led live training in South Africa (online or onsite) is aimed at intermediate-level professionals who wish to gain practical skills in customizing AI models for critical financial tasks.
By the end of this training, participants will be able to:
- Understand the fundamentals of fine-tuning for finance applications.
- Leverage pre-trained models for domain-specific tasks in finance.
- Apply techniques for fraud detection, risk assessment, and financial advice generation.
- Ensure compliance with financial regulations such as GDPR and SOX.
- Implement data security and ethical AI practices in financial applications.
Fine-Tuning Models and Large Language Models (LLMs)
14 HoursThis 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.
Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)
14 HoursThis 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.
Fine-Tuning Multimodal Models
28 HoursThis 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.
Fine-Tuning for Natural Language Processing (NLP)
21 HoursThis 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.
Fine-Tuning AI for Financial Services: Risk Prediction and Fraud Detection
14 HoursThis 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.
Fine-Tuning AI for Healthcare: Medical Diagnosis and Predictive Analytics
14 HoursThis 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.
Fine-Tuning DeepSeek LLM for Custom AI Models
21 HoursThis 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.
Fine-Tuning Defense AI for Autonomous Systems and Surveillance
14 HoursThis 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.
Fine-Tuning Legal AI Models: Contract Review and Legal Research
14 HoursThis 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.
Fine-Tuning Large Language Models Using QLoRA
14 HoursThis 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.
