Domain-Specific Fine-Tuning for Finance Training Course

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

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

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

• Applying supervised fine-tuning techniques to Gemini models in Vertex AI.
• Implementing prompt management workflows that include versioning and testing.
• Utilizing evaluation libraries to benchmark and optimize AI performance.
• Deploying and monitoring enhanced models in production environments.

Course Format

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

Customization Options

• For customized training on this topic, please contact us to arrange.

This instructor-led live training, available online or onsite, targets advanced-level machine learning professionals aiming to master cutting-edge transfer learning techniques and apply them to complex real-world problems.

By the conclusion 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.

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

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

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

This instructor-led, live training in Argentina (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 Argentina (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 Argentina (online or on-site) is tailored for intermediate developers and AI practitioners aiming to implement fine-tuning strategies for large models without relying on heavy computational resources.

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

• Comprehend the core principles of Low-Rank Adaptation (LoRA).
• Utilize LoRA for the efficient fine-tuning of large models.
• Optimize fine-tuning for environments with constrained resources.
• Evaluate and deploy LoRA-enhanced models for practical implementation.

This instructor-led, live training in Argentina (online or on-site) 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 Argentina (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.

This instructor-led, live training in Argentina (online or on-site) is designed for advanced-level data scientists and AI engineers in the financial industry who want to optimize models for applications like credit scoring, fraud detection, and risk modeling using specialized financial data.

Upon completing this training, participants will be able to:

• Optimize AI models using financial datasets to improve fraud and risk prediction.
• Implement techniques such as transfer learning, LoRA, and regularization to boost model efficiency.
• Incorporate financial compliance requirements into the AI modeling workflow.
• Deploy optimized models for production use within financial services platforms.

This instructor-led, live training in Argentina (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 patient outcome forecasting using structured and unstructured medical data.

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

• Fine-tuning AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Implementing transfer learning, domain adaptation, and model compression techniques within medical contexts.
• Addressing privacy concerns, bias, and regulatory compliance during model development.
• Deploying and monitoring fine-tuned models in real-world healthcare environments.

This instructor-led, live training in Argentina (online or onsite) targets advanced AI researchers, machine learning engineers, and developers aiming 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 Argentina (online or onsite) is designed for advanced-level defense AI engineers and military technology developers seeking to fine-tune deep learning models for autonomous vehicles, drones, and surveillance systems, adhering to rigorous 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 Argentina (online or in-person) is aimed at intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research in 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 Argentina (online or onsite) is tailored for intermediate to advanced-level machine learning engineers, AI developers, and data scientists seeking to learn how to utilize QLoRA for the efficient fine-tuning of large models for specific tasks and customizations.

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

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

This instructor-led, live training in Argentina (online or onsite) targets 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.