Fine-Tuning Training in Lyon

This instructor-led, live training in Lyon (online or on-site) is designed for advanced defense AI engineers and military technology developers who wish to fine-tune deep learning models for autonomous vehicles, drones, and surveillance systems while meeting strict security and reliability standards.

Upon completing 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 dynamic environments and varying 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 Lyon (online or onsite) targets intermediate-level legal tech engineers and AI developers aiming to fine-tune language models for tasks such as contract analysis, clause extraction, and automated legal research within legal service environments.

Upon completing this training, participants will be capable of:

• Preparing and cleaning legal documents for fine-tuning NLP models.
• Applying fine-tuning strategies to enhance 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 Lyon (online or onsite) targets intermediate to advanced-level medical AI developers and data scientists who wish to refine models for clinical diagnosis, disease prediction, and patient outcome forecasting using 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 contexts.
• Address privacy, bias, and regulatory compliance in model development.
• Deploy and monitor refined models in real-world healthcare environments.

This instructor-led, live training in Lyon (online or onsite) is designed for advanced-level data scientists and AI engineers in the financial sector who aim to refine models for applications such as credit scoring, fraud detection, and risk modeling using specialized financial data.

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

• Fine-tuning AI models on financial datasets to enhance fraud and risk prediction.
• Utilizing techniques like transfer learning, LoRA, and regularization to improve model efficiency.
• Incorporating financial compliance requirements into the AI modeling process.
• Deploying fine-tuned models for production use within financial services platforms.

This instructor-led, live training in Lyon (online or onsite) targets advanced AI maintenance engineers and MLOps professionals seeking 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.

This instructor-led, live training in Lyon (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists who wish 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 Lyon (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 Lyon (online or onsite) is aimed at 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 Lyon (available online or onsite) is designed for intermediate-level NLP engineers and knowledge management teams who aim to fine-tune RAG pipelines to improve performance in question answering, enterprise search, and summarization scenarios.

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 Lyon (available online or onsite) is designed for intermediate-level machine learning practitioners and AI developers who wish to customize and deploy open-weight models such as LLaMA, Mistral, and Qwen for specific business or internal applications.

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

• Gaining a comprehensive understanding of the open-source LLM ecosystem and the distinctions between various models.
• Preparing datasets and configuring fine-tuning parameters for models like LLaMA, Mistral, and Qwen.
• Executing fine-tuning pipelines using Hugging Face Transformers and PEFT.
• Evaluating, saving, and deploying fine-tuned models within secure environments.

This instructor-led, live training in Lyon (online or on-site) targets intermediate data scientists and AI engineers who aim to fine-tune large language models more affordably and efficiently using techniques such as LoRA, Adapter Tuning, and Prefix Tuning.

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

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

This instructor-led, live training in Lyon (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 customizations.

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

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

This instructor-led, live training offered Lyon (online or onsite) is tailored for advanced machine learning engineers and AI researchers looking to apply RLHF to fine-tune large AI models for improved performance, safety, and alignment.

Upon completing this training, participants will be equipped to:

• Grasp the theoretical underpinnings of RLHF and its critical role in contemporary AI development.
• Develop reward models driven by human feedback to steer reinforcement learning workflows.
• Fine-tune large language models using RLHF methodologies to ensure their outputs align with human preferences.
• Implement industry best practices for scaling RLHF processes within production-ready AI infrastructure.

This instructor-led, live training in Lyon (online or onsite) is designed for 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:

• Comprehend the core principles of fine-tuning for financial applications.
• Utilize pre-trained models for domain-specific tasks within the finance industry.
• Apply techniques for fraud detection, risk assessment, and generating financial advice.
• Ensure adherence to financial regulations, including GDPR and SOX.
• Implement robust data security measures and ethical AI practices in financial applications.

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

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

• Diagnose issues like overfitting, underfitting, and data imbalance.
• Implement strategies to improve model convergence.
• Optimize fine-tuning pipelines for better performance.
• Debug training processes using practical tools and techniques.

This instructor-led, live training in Lyon (online or onsite) is designed for advanced professionals who wish 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:

• Comprehend the challenges associated with fine-tuning large models.
• Implement distributed training techniques on large models.
• Utilize model quantization and pruning to enhance efficiency.
• Maximize hardware utilization for fine-tuning tasks.
• Effectively deploy fine-tuned models within production environments.

This instructor-led, live training in Lyon (available online or onsite) is targeted at intermediate-level professionals aiming to leverage prompt engineering and few-shot learning to enhance LLM performance for real-world applications.

Upon completion 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 Lyon (online or onsite) is designed for advanced professionals seeking to master multimodal model fine-tuning for innovative AI solutions.

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

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

This instructor-led live training in Lyon (online or onsite) targets advanced-level AI researchers, machine learning engineers, and developers who want to fine-tune DeepSeek LLM models to create specialized 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.
• Optimize and deploy fine-tuned models efficiently.

This instructor-led live training in Lyon (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 Lyon (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.

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.

Vertex AI offers sophisticated tools for fine-tuning large language models and managing prompts, allowing developers and data teams to enhance model accuracy, streamline iterative workflows, and ensure rigorous evaluation through integrated libraries and services.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced practitioners looking to boost the performance and reliability of generative AI applications by utilizing supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.

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

• Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implement prompt management workflows, including version control and testing.
• Utilize evaluation libraries to benchmark and optimize AI performance.
• Deploy and monitor enhanced models in production environments.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on Vertex AI fine-tuning and prompt tools.
• Case studies showcasing enterprise model optimization.

Customization Options

• To request customized training for this course, please contact us to arrange it.

This instructor-led live training in Lyon (online or on-site) is designed for beginner to intermediate-level machine learning professionals seeking to understand and apply transfer learning techniques to boost 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.

This instructor-led, live training in Lyon (online or on-site) targets intermediate developers and AI practitioners who aim to implement fine-tuning strategies for large models without relying on 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 Lyon (online or onsite) is aimed at intermediate-level to advanced-level 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 NLP tasks.
• Optimize model performance and address common challenges.

This instructor-led, live training in Lyon (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:

• Grasp the fundamentals of fine-tuning for NLP tasks.
• Apply fine-tuning techniques to pre-trained models like GPT, BERT, and T5 for targeted NLP applications.
• Tune hyperparameters to boost model performance.
• Assess and deploy fine-tuned models in practical, real-world settings.