Fine-Tuning Defense AI for Autonomous Systems and Surveillance Training Course

This instructor-led training in the UK delves into advanced fine-tuning and prompt management within Vertex AI. It supports intermediate and advanced practitioners in optimising Gemini models, instituting rigorous evaluation workflows, and deploying robust generative AI solutions tailored for real-world enterprise contexts.

This instructor-led, live training in the UK (online or in-person) is tailored for senior machine learning specialists who aim to perfect state-of-the-art transfer learning strategies and apply them to intricate real-world issues.

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

• Grasping the advanced theories and practices surrounding transfer learning.
• Executing domain-specific adaptation methods on pre-trained architectures.
• Utilising continuous learning to handle dynamic tasks and shifting data.
• Applying multi-task fine-tuning to boost model efficacy across multiple objectives.

This instructor-led live session in the UK (online or in-person) is designed for senior AI maintenance engineers and MLOps specialists aiming to establish resilient ongoing learning pipelines and effective refresh strategies for deployed, fine-tuned models.

Upon completion, delegates will be equipped to:

• Architect and execute ongoing learning workflows for models in production environments.
• Counteract catastrophic forgetting through rigorous training protocols and memory governance.
• Automate surveillance and update triggers in response to model drift or shifts in data.
• Embed model refresh strategies within established CI/CD and MLOps pipelines.

This instructor-led, live training in the UK (online or onsite) is tailored for advanced-level professionals aiming 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 live, instructor-led training in the UK (available online or onsite) is tailored for intermediate-level professionals aiming to acquire practical skills in customising AI models for critical financial tasks.

By the end of this training, participants will be capable of:

• Understanding the fundamentals of fine-tuning for financial applications.
• Leveraging pre-trained models for domain-specific tasks in finance.
• Applying techniques for fraud detection, risk assessment, and generating financial advice.
• Ensuring compliance with financial regulations such as GDPR and SOX.
• Implementing data security and ethical AI practices in financial applications.

This instructor-led, live training in the UK (available online or on-site) is tailored for intermediate to advanced professionals aiming to adapt pre-trained models to specific tasks and datasets.

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

• Comprehend the core principles of fine-tuning and its diverse applications.
• Prepare and structure datasets for the fine-tuning of pre-trained models.
• Adapt large language models (LLMs) for NLP-specific tasks.
• Optimise model performance and resolve typical implementation challenges.

This instructor-led live training in the UK (online or on-site) is tailored for intermediate developers and AI practitioners seeking to implement fine-tuning strategies for large models without requiring extensive computational resources.

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

• Grasp the fundamental principles of Low-Rank Adaptation (LoRA).
• Apply LoRA to achieve efficient fine-tuning of large models.
• Optimize the fine-tuning process for resource-constrained settings.
• Assess and deploy LoRA-tuned models for real-world applications.

Delivered by an instructor in the UK either virtually or in person, this programme is designed for seasoned professionals looking to perfect the adaptation of multimodal systems for cutting-edge artificial intelligence applications.

Upon completion, participants will be equipped to:

• Grasp the structural design of prominent frameworks such as CLIP and Flamingo.
• Curate and pre-process multimodal data with precision.
• Adapt multimodal models to address specific operational requirements.
• Enhance model performance for practical deployment and real-world utility.

This instructor-led, live training in the UK (online or on-site) is tailored for intermediate professionals aiming to refine their NLP projects through the efficient 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 course, held at the UK (whether online or in person), is tailored for advanced data scientists and AI engineers in the financial industry. It equips them to fine-tune models for key applications like credit scoring, fraud detection, and risk modelling, utilising domain-specific financial data.

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

• Fine-tune AI models on financial datasets to improve fraud and risk prediction capabilities.
• Apply methodologies such as transfer learning, LoRA, and regularization to increase model efficiency.
• Incorporate financial compliance considerations into the AI modelling process.
• Deploy fine-tuned models for production use in financial services platforms.

This instructor-led, live training in the UK (online or onsite) is tailored for intermediate to advanced medical AI developers and data scientists aiming to refine models for clinical diagnosis, disease prediction, and patient outcome forecasting, leveraging structured and unstructured medical data.

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

• Refine AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Apply transfer learning, domain adaptation, and model compression in medical settings.
• Address privacy, bias, and regulatory compliance in model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

This instructor-led live training in the UK (delivered either online or on-site) is tailored for advanced-level AI researchers, machine learning engineers, and developers aiming to fine-tune DeepSeek LLM models. The objective is to create specialised AI applications suited to particular industries, domains, or business needs.

By the conclusion of this training, participants will have the ability to:

• Comprehend the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Curate datasets and preprocess data for fine-tuning.
• Fine-tune the DeepSeek LLM for specific domain applications.
• Optimise and deploy fine-tuned models with efficiency.

This instructor-led, live training in the UK (online or onsite) is tailored for intermediate-level legal tech engineers and AI developers who aim to optimise language models for tasks such as contract analysis, clause extraction, and automated legal research in legal service environments.

By the end of this training, participants will be capable of:

• Preparing and cleaning legal documents for fine-tuning NLP models.
• Applying fine-tuning strategies to boost model accuracy on legal tasks.
• Deploying models to assist with contract review, classification, and research.
• Ensuring compliance, auditability, and traceability of AI outputs in legal contexts.

This instructor-led, live training in the UK (available online or in-person) is tailored for mid-level to advanced machine learning engineers, AI developers, and data scientists aiming to utilise QLoRA for the efficient fine-tuning of large models to meet specific operational tasks and customisation needs.

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

• Understand the theoretical basis of QLoRA and quantisation techniques for LLMs.
• Implement QLoRA in the fine-tuning of large language models for domain-specific applications.
• Optimise fine-tuning performance under limited computational constraints using quantisation.
• Deploy and evaluate fine-tuned models effectively in real-world environments.

This instructor-led, live training in the UK (online or on-site) is tailored for intermediate-level embedded AI developers and edge computing specialists looking to fine-tune and optimise lightweight AI models for deployment on resource-limited devices.

By the end of the session, participants will be able to:

• Choose and adapt pre-trained models appropriate for edge deployment.
• Use compression methods like quantization and pruning to reduce model size and latency.
• Apply transfer learning to fine-tune models for improved task-specific outcomes.
• Deploy optimised models on actual edge hardware platforms.