Advanced Techniques in Transfer Learning Training Course

This 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.

This 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.

This 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.

This 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.

This 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.

This 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.

This 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.

This 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.

This instructor-led, live training in US Empire (online or onsite) is aimed at intermediate-level machine learning practitioners and artificial intelligence developers who wish to fine-tune and deploy open-weight models such as LLaMA, Mistral, and Qwen for specific business or internal applications.

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

• Understand the ecosystem and differences between open-source large language models (LLMs).
• Prepare datasets and fine-tuning configurations for models like LLaMA, Mistral, and Qwen.
• Execute fine-tuning pipelines using Hugging Face Transformers and PEFT.
• Evaluate, save, and deploy fine-tuned models in secure environments, ensuring alignment with public sector workflows and governance requirements for government.

This 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.

This 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.

This 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.

This 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.

This 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.

This 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.