Domain-Specific Fine-Tuning for Finance Training Course

Vertex AI equips developers and data teams with sophisticated tools for fine-tuning large language models and managing prompts. These capabilities allow teams to enhance model accuracy, streamline iterative workflows, and uphold rigorous evaluation standards through integrated libraries and services.

This instructor-led training, available online or onsite, is designed for intermediate to advanced practitioners seeking to boost the performance and reliability of generative AI applications. Participants will gain expertise in supervised fine-tuning, prompt versioning, and evaluation services within the Vertex AI ecosystem.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures and discussions.
• Hands-on labs focusing on Vertex AI fine-tuning and prompt tools.
• Enterprise case studies highlighting model optimization strategies.

Customization Options

• To arrange customized training for this course, please contact us directly.

This instructor-led, live training in India (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.

This instructor-led, live training in India (online or onsite) is designed for advanced-level AI maintenance engineers and MLOps professionals who want 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 India (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.

Presented as an instructor-led, live session in India (online or onsite), this training targets intermediate to advanced professionals aiming to customize pre-trained models for specific tasks and datasets.

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

• Grasp the foundational principles of fine-tuning and its practical applications.
• Effectively prepare datasets for fine-tuning pre-trained models.
• Fine-tune Large Language Models (LLMs) specifically for Natural Language Processing (NLP) tasks.
• Optimize model performance and resolve common technical challenges.

This instructor-led live training in India (online or onsite) caters to intermediate-level developers and AI practitioners looking to implement fine-tuning strategies for large models without the burden of heavy computational resources.

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

• Understand the fundamental principles of Low-Rank Adaptation (LoRA).
• Implement LoRA for the efficient fine-tuning of large models.
• Optimise fine-tuning workflows for environments with limited resources.
• Evaluate and deploy LoRA-adapted models for practical use cases.

This instructor-led, live training in India (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 India (online or onsite) is designed for intermediate-level professionals looking 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.
• Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
• Optimize hyperparameters to improve model performance.
• Evaluate and deploy fine-tuned models in real-world scenarios.

This instructor-led, live training in India (online or onsite) is designed for advanced-level data scientists and AI engineers in the financial sector who aim 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 to enhance fraud and risk prediction.
• Apply techniques such as transfer learning, LoRA, and regularization to improve 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 India (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who want to refine models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

Upon completing this training, participants will be equipped to:

• Refine AI models using healthcare datasets, including EMRs, imaging, and time-series data.
• Implement transfer learning, domain adaptation, and model compression within medical contexts.
• Manage privacy, bias, and regulatory compliance during model development.
• Deploy and monitor refined models in practical healthcare settings.

This instructor-led, live training in India (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 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 India (online or onsite) is designed for advanced-level defense AI engineers and military technology developers who wish to fine-tune deep learning models for autonomous vehicles, drones, and surveillance systems while 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 India (online or onsite) is designed for 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 India (online or onsite) is tailored for intermediate to advanced 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 custom implementations.

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

• Comprehend the theory behind QLoRA and quantization techniques for LLMs.
• Apply QLoRA to fine-tune large language models for domain-specific applications.
• Enhance fine-tuning performance on limited computational resources through quantization.
• Efficiently deploy and evaluate fine-tuned models in real-world applications.

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

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

• Identifying and adapting pre-trained models appropriate for edge deployment.
• Applying quantization, pruning, and other compression techniques to minimize model size and latency.
• Fine-tuning models using transfer learning to achieve task-specific performance.
• Deploying optimized models onto actual edge hardware platforms.