Safety and Bias Mitigation in Fine-Tuned Models Training Course
Safety and Bias Mitigation in Fine-Tuned Models is an increasing concern as artificial intelligence becomes more integrated into decision-making processes across industries, and regulatory standards continue to evolve.
This instructor-led, live training (online or onsite) is designed for intermediate-level machine learning engineers and AI compliance professionals who aim to identify, evaluate, and reduce safety risks and biases in fine-tuned language models for government applications.
By the end of this training, participants will be able to:
- Understand the ethical and regulatory context for safe AI systems within public sector workflows.
- Identify and assess common forms of bias in fine-tuned models used in governmental contexts.
- Apply bias mitigation techniques during and after model training for government use cases.
- Design and audit models to ensure safety, transparency, and fairness in alignment with public sector governance and accountability standards.
Format of the Course
- Interactive lecture and discussion tailored for government professionals.
- Extensive exercises and practice sessions relevant to government applications.
- Hands-on implementation in a live-lab environment specific to public sector needs.
Course Customization Options
- To request a customized training for this course, tailored specifically for government agencies, please contact us to arrange.
Course Outline
Foundations of Safe and Fair AI for Government
- Key concepts: safety, bias, fairness, transparency
- Types of bias: dataset, representation, algorithmic
- Overview of regulatory frameworks (EU AI Act, GDPR, etc.)
Bias in Fine-Tuned Models for Government
- 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 for Government
- Data-level strategies (rebalancing, augmentation)
- In-training strategies (regularization, adversarial debiasing)
- Post-processing strategies (output filtering, calibration)
Model Safety and Robustness for Government
- Detecting unsafe or harmful outputs
- Adversarial input handling
- Red teaming and stress testing fine-tuned models
Auditing and Monitoring AI Systems for Government
- Bias and fairness evaluation metrics (e.g., demographic parity)
- Explainability tools and transparency frameworks
- Ongoing monitoring and governance practices
Toolkits and Hands-On Practice for Government
- 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 for Government
- Best practices for integrating safety in LLM workflows
- Documentation and model cards for compliance
- Preparing for audits and external reviews
Summary and Next Steps for Government
Requirements
- An understanding of machine learning models and the training processes for government applications
- Experience working with fine-tuning and large language models (LLMs)
- Familiarity with Python programming and natural language processing (NLP) concepts
Audience
- AI compliance teams for government
- Machine learning engineers for government
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
Safety and Bias Mitigation in Fine-Tuned Models Training Course - Booking
Safety and Bias Mitigation in Fine-Tuned Models Training Course - Enquiry
Safety and Bias Mitigation in Fine-Tuned Models - Consultancy Enquiry
Consultancy Enquiry
Upcoming Courses
Related Courses
Advanced Techniques in Transfer Learning
14 HoursThis instructor-led, live training in US Empire (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 for government.
By the end of this training, participants will be able to:
- Understand advanced concepts and methodologies in transfer learning for government applications.
- Implement domain-specific adaptation techniques for pre-trained models to address specific public sector challenges.
- Apply continual learning strategies to manage evolving tasks and datasets within government workflows.
- Master multi-task fine-tuning to enhance model performance across various governmental tasks.
Deploying Fine-Tuned Models in Production
21 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently for government use.
By the end of this training, participants will be able to:
- Comprehend the challenges associated with deploying fine-tuned models in production environments.
- Containerize and deploy models using tools such as Docker and Kubernetes, ensuring alignment with public sector workflows.
- Implement robust monitoring and logging practices for deployed models to enhance governance and accountability.
- Optimize models for latency and scalability to meet the demands of real-world government applications.
Domain-Specific Fine-Tuning for Finance
21 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level professionals who wish to gain practical skills in customizing AI models for critical financial tasks for government.
By the end of this training, participants will be able to:
- Understand the fundamentals of fine-tuning AI models for finance applications.
- Leverage pre-trained models for domain-specific tasks in financial management.
- Apply techniques for fraud detection, risk assessment, and financial advice generation within government contexts.
- Ensure compliance with relevant financial regulations such as GDPR and SOX.
- Implement data security and ethical AI practices in financial applications for government use.
Fine-Tuning Models and Large Language Models (LLMs)
14 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at intermediate to advanced professionals who wish to customize pre-trained models for specific tasks and datasets for government use.
By the end of this training, participants will be able to:
- Understand the principles of fine-tuning and their applications in public sector workflows.
- Prepare datasets for fine-tuning pre-trained models to meet government-specific requirements.
- Fine-tune large language models (LLMs) for natural language processing tasks relevant to government operations.
- Optimize model performance and address common challenges to ensure compliance with governance standards.
Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)
14 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level developers and AI practitioners who wish to implement fine-tuning strategies for large models without requiring extensive computational resources.
By the end of this training, participants will be able to:
- Understand the principles of Low-Rank Adaptation (LoRA).
- Implement LoRA to efficiently fine-tune large models.
- Optimize fine-tuning processes for environments with limited resources.
- Evaluate and deploy LoRA-tuned models for practical applications, ensuring alignment with public sector workflows and governance requirements for government.
Fine-Tuning Multimodal Models
28 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at advanced-level professionals who wish to master the fine-tuning of multimodal models for innovative AI solutions for government.
By the end of this training, participants will be able to:
- Comprehend the architecture of multimodal models such as CLIP and Flamingo.
- Effectively prepare and preprocess multimodal datasets.
- Fine-tune multimodal models for specific tasks relevant to government applications.
- Optimize models for real-world performance and deployment in public sector environments.
Fine-Tuning for Natural Language Processing (NLP)
21 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level professionals who wish to enhance their natural language processing (NLP) projects through the effective fine-tuning of pre-trained language models for government applications.
By the end of this training, participants will be able to:
- Understand the foundational principles of fine-tuning for NLP tasks.
- Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications relevant to government operations.
- Optimize hyperparameters to achieve enhanced model performance in government contexts.
- Evaluate and deploy fine-tuned models in real-world government scenarios.
Fine-Tuning DeepSeek LLM for Custom AI Models
21 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at advanced-level AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to create specialized AI applications tailored for government, 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 in alignment with public sector workflows.
- Fine-tune DeepSeek LLM for domain-specific applications to meet the unique requirements of government agencies.
- Optimize and deploy fine-tuned models efficiently, ensuring compliance with governance and accountability standards.
Fine-Tuning Large Language Models Using QLoRA
14 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at intermediate to advanced machine learning engineers, AI developers, and data scientists who wish to learn how to use QLoRA to effectively fine-tune large models for specific tasks and customizations.
By the end of this training, participants will be able to:
- Understand the theoretical foundations of QLoRA and quantization techniques for large language models.
- Implement QLoRA in the process of fine-tuning large language models for domain-specific applications.
- Optimize fine-tuning performance on limited computational resources through the use of quantization.
- Deploy and evaluate fine-tuned models efficiently in real-world scenarios, enhancing capabilities for government and public sector workflows.
Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)
14 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at advanced-level machine learning engineers and artificial intelligence researchers who wish to apply Reinforcement Learning with Human Feedback (RLHF) to fine-tune large AI models for enhanced performance, safety, and alignment.
By the end of this training, participants will be able to:
- Understand the theoretical foundations of RLHF and its critical role in modern AI development for government applications.
- Implement reward models based on human feedback to guide reinforcement learning processes effectively.
- Fine-tune large language models using RLHF techniques to ensure outputs align with human preferences and public sector standards.
- Apply best practices for scaling RLHF workflows to support production-grade AI systems in government environments.
Optimizing Large Models for Cost-Effective Fine-Tuning
21 HoursThis instructor-led, live training in US Empire (online or onsite) is designed for advanced-level professionals who wish to master techniques for optimizing large models for cost-effective fine-tuning in real-world scenarios, tailored specifically for government applications.
By the end of this training, participants will be able to:
- Understand the challenges associated with fine-tuning large models for government use cases.
- Apply distributed training techniques to enhance the efficiency of large models in public sector workflows.
- Leverage model quantization and pruning methods to improve performance and reduce resource consumption.
- Optimize hardware utilization to support cost-effective and efficient fine-tuning tasks for government operations.
- Deploy fine-tuned models effectively in production environments, ensuring alignment with public sector governance and accountability requirements.
Prompt Engineering and Few-Shot Fine-Tuning
14 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimize large language model (LLM) performance for real-world government applications.
By the end of this training, participants will be able to:
- Understand the principles of prompt engineering and few-shot learning for government use cases.
- Design effective prompts for various natural language processing tasks relevant to public sector workflows.
- Leverage few-shot techniques to adapt LLMs with minimal data, ensuring alignment with governance and accountability standards.
- Optimize LLM performance for practical applications within the public sector.
Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs
14 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level data scientists and AI engineers who wish to fine-tune large language models more affordably and efficiently using methods such as LoRA, Adapter Tuning, and Prefix Tuning.
By the end of this training, participants will be able to:
- Comprehend the theory behind parameter-efficient fine-tuning approaches.
- Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT for government applications.
- Evaluate the performance and cost trade-offs of PEFT methods compared to full fine-tuning.
- Deploy and scale fine-tuned LLMs with minimized compute and storage requirements.
Introduction to Transfer Learning
14 HoursThis instructor-led, live training in US Empire (online or onsite) is aimed at beginner-level to intermediate-level machine learning professionals who wish to understand and apply transfer learning techniques to enhance efficiency and performance in AI projects for government.
By the end of this training, participants will be able to:
- Understand the core concepts and benefits of transfer learning for government applications.
- Explore popular pre-trained models and their potential uses in public sector workflows.
- Perform fine-tuning of pre-trained models to meet specific governmental tasks.
- Apply transfer learning methodologies to address real-world challenges in natural language processing (NLP) and computer vision within the government context.
Troubleshooting Fine-Tuning Challenges
14 HoursThis instructor-led, live training in US Empire (online or onsite) is designed for advanced-level professionals who seek to enhance their capabilities in diagnosing and addressing fine-tuning challenges for machine learning models for government use.
By the end of this training, participants will be able to:
- Identify issues such as overfitting, underfitting, and data imbalance.
- Implement strategies to improve model convergence and reliability.
- Optimize fine-tuning processes for enhanced performance in government applications.
- Utilize practical tools and techniques to debug training processes effectively.
