Fine-Tuning Training in Gaia

This instructor-led live training in Gaia (online or onsite) is targeted at advanced defense AI engineers and military technology developers who wish to fine-tune deep learning models for autonomous vehicles, drones, and surveillance systems, meeting stringent security and reliability standards.

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

• Fine-tune computer vision and sensor fusion models for surveillance and targeting tasks.
• Adapt autonomous AI systems to changing environments and mission profiles.
• Implement robust validation and fail-safe mechanisms in model pipelines.
• Ensure alignment with defense-specific compliance, safety, and security standards.

This instructor-led, live training in Gaia (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.

By the end 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 Gaia (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim to fine-tune models for clinical diagnosis, disease prediction, and forecasting patient outcomes using structured and unstructured medical data.

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

• Fine-tune AI models using healthcare datasets, including Electronic Medical Records (EMRs), imaging data, and time-series data.
• Implement transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Address privacy concerns, bias mitigation, and regulatory compliance during model development.
• Deploy and monitor fine-tuned models in real-world healthcare settings.

This instructor-led, live training in Gaia (online or onsite) is aimed at advanced-level data scientists and AI engineers in the financial sector who wish to fine-tune models for applications such as credit scoring, fraud detection, and risk modeling using domain-specific financial data.

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

• Fine-tune AI models on financial datasets for improved fraud and risk prediction.
• Apply techniques such as transfer learning, LoRA, and regularization to enhance model efficiency.
• Integrate financial compliance considerations into the AI modeling workflow.
• Deploy fine-tuned models for production use in financial services platforms.

This instructor-led, live training in Gaia (online or onsite) is designed for advanced-level AI maintenance engineers and MLOps professionals seeking to implement robust continual learning pipelines and effective update strategies for deployed, fine-tuned models.

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

• Design and implement continual learning workflows for deployed models.
• Mitigate catastrophic forgetting through effective training and memory management.
• Automate monitoring and update triggers in response to model drift or data changes.
• Integrate model update strategies into existing CI/CD and MLOps pipelines.

This instructor-led, live training in Gaia (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists who want to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

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

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantization, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning for task-specific performance.
• Deploy optimized models on real edge hardware platforms.

This instructor-led, live training in Gaia (online or onsite) is aimed at advanced-level computer vision engineers and AI developers who wish to fine-tune VLMs such as CLIP and Flamingo to improve performance on industry-specific visual-text tasks.

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

• Understand the architecture and pretraining methods of vision-language models.
• Fine-tune VLMs for classification, retrieval, captioning, or multimodal QA.
• Prepare datasets and apply PEFT strategies to reduce resource usage.
• Evaluate and deploy customized VLMs in production environments.

This instructor-led, live training in Gaia (online or onsite) is designed for intermediate-level ML engineers and AI compliance professionals who wish to identify, evaluate, and reduce safety risks and biases in fine-tuned language models.

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

• Understand the ethical and regulatory context for safe AI systems.
• Identify and evaluate common forms of bias in fine-tuned models.
• Apply bias mitigation techniques during and after training.
• Design and audit models for safety, transparency, and fairness.

This instructor-led, live training in Gaia (online or onsite) is designed for intermediate-level NLP engineers and knowledge management teams who wish to fine-tune RAG pipelines to enhance performance in question answering, enterprise search, and summarization use cases.

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

• Understand the architecture and workflow of RAG systems.
• Fine-tune retriever and generator components for domain-specific data.
• Evaluate RAG performance and apply improvements through PEFT techniques.
• Deploy optimized RAG systems for internal or production use.

This instructor-led, live training in Gaia (online or onsite) is aimed at intermediate-level ML practitioners and AI developers who wish to fine-tune and deploy open-weight models like 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 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.

This live, instructor-led training in Gaia (offered online or onsite) is designed for intermediate-level data scientists and AI engineers aiming to fine-tune large language models with improved affordability and efficiency, employing techniques such as LoRA, Adapter Tuning, and Prefix Tuning.

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

• Understand the theoretical underpinnings of parameter-efficient fine-tuning approaches.
• Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
• Analyze the performance and cost trade-offs of PEFT methods compared to full fine-tuning.
• Deploy and scale fine-tuned LLMs with reduced compute and storage requirements.

This instructor-led, live training in Gaia (online or on-site) is designed for intermediate to advanced-level machine learning engineers, AI developers, and data scientists who wish to learn how to employ QLoRA to efficiently fine-tune large models for specific tasks and customisations.

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

• Comprehend the theory behind QLoRA and quantization techniques for LLMs.
• Implement QLoRA for fine-tuning large language models in domain-specific applications.
• Optimize fine-tuning performance on limited computational resources through quantization.
• Deploy and evaluate fine-tuned models in real-world applications efficiently.

This live, instructor-led course in Gaia, offered online or onsite, caters to advanced machine learning engineers and AI researchers looking to apply RLHF for fine-tuning large AI models to achieve enhanced performance, safety, and alignment.

By the conclusion of the training, participants will have the capability to:

• Comprehend the theoretical basis of RLHF and its necessity in modern AI contexts.
• Create reward models based on human input to direct reinforcement learning.
• Use RLHF techniques to adjust large language models so that their outputs align with human expectations.
• Employ best practices for expanding RLHF pipelines for production-ready AI systems.

This instructor-led live training in Gaia (online or on-site) is tailored for intermediate professionals aiming to build practical skills in customising AI models for critical financial functions.

Upon completing this training, participants will be able to:

• Comprehend the basics of fine-tuning for financial applications.
• Apply pre-trained models to domain-specific financial tasks.
• Implement techniques for fraud detection, risk evaluation, and the generation of financial advice.
• Guarantee compliance with financial regulations such as GDPR and SOX.
• Establish data security and ethical AI practices within financial applications.

This instructor-led, live training in Gaia (online or onsite) is designed for advanced professionals who wish to refine their skills in diagnosing and solving fine-tuning challenges for machine learning models.

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

• Identify problems such as overfitting, underfitting, and data imbalance.
• Apply strategies to enhance model convergence.
• Optimize fine-tuning pipelines for improved performance.
• Debug training processes using practical tools and techniques.

This instructor-led, live training in Gaia (online or onsite) is designed for advanced professionals who want to master techniques for optimizing large models for cost-effective fine-tuning in real-world scenarios.

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

• Understand the challenges of fine-tuning large models.
• Apply distributed training techniques to large models.
• Leverage model quantization and pruning for efficiency.
• Optimize hardware utilization for fine-tuning tasks.
• Deploy fine-tuned models effectively in production environments.

This instructor-led, live training in Gaia (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimize LLM performance for real-world applications.

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

• Understand the principles of prompt engineering and few-shot learning.
• Design effective prompts for various NLP tasks.
• Leverage few-shot techniques to adapt LLMs with minimal data.
• Optimize LLM performance for practical applications.

This instructor-led, live training in Gaia (online or onsite) is aimed at advanced-level professionals who wish to master multimodal model fine-tuning for innovative AI solutions.

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

• Understand the architecture of multimodal models like CLIP and Flamingo.
• Prepare and preprocess multimodal datasets effectively.
• Fine-tune multimodal models for specific tasks.
• Optimize models for real-world applications and performance.

This instructor-led live training in Gaia (online or onsite) targets advanced-level AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to create specialized AI applications tailored to specific industries, domains, or business needs.

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

• Comprehending the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
• Preparing datasets and performing data preprocessing for fine-tuning.
• Fine-tuning DeepSeek LLM for domain-specific applications.
• Efficiently optimizing and deploying fine-tuned models.

This instructor-led, live training in Gaia (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 Gaia (online or onsite) is designed for advanced-level machine learning professionals aiming to master cutting-edge transfer learning techniques and apply them to complex real-world challenges.

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

• Grasp 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 various tasks.

This instructor-led training in Gaia explores advanced fine-tuning and prompt management within Vertex AI. It assists intermediate and advanced professionals in optimising Gemini models, establishing rigorous evaluation workflows, and deploying dependable generative AI solutions for real-world enterprise applications.

This instructor-led, live training in Gaia (online or onsite) is aimed at beginner-level to intermediate-level machine learning professionals who wish to understand and apply transfer learning techniques to improve efficiency and performance in AI projects.

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

• Understand the core concepts and benefits of transfer learning.
• Explore popular pre-trained models and their applications.
• Perform fine-tuning of pre-trained models for custom tasks.
• Apply transfer learning to solve real-world problems in NLP and computer vision.

Delivered as an instructor-led live training in Gaia (online or onsite), this course is aimed at 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 Gaia (online or onsite) is designed for intermediate to advanced professionals who wish to customize pre-trained models for specific tasks and datasets.

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

• Understand the principles of fine-tuning and its applications.
• Prepare datasets for fine-tuning pre-trained models.
• Fine-tune Large Language Models (LLMs) for Natural Language Processing (NLP) tasks.
• Optimize model performance and address common challenges.

This instructor-led, live training in Gaia (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.