Online or onsite, instructor-led live Fine-Tuning training courses demonstrate through interactive hands-on practice how to use customized machine learning models to optimize performance for specific tasks, datasets, or applications.
Fine-Tuning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Fine-Tuning training can be carried out locally on customer premises in Quito or in NobleProg corporate training centers in Quito.
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Quito - Av Eloy Alfaro
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This instructor-led, live training in Quito (online or onsite) is aimed at intermediate-level data scientists and AI engineers who wish to fine-tune large language models more affordably and efficiently using methods like LoRA, Adapter Tuning, and Prefix Tuning.By the end of this training, participants will be able to:
Understand the theory behind parameter-efficient fine-tuning approaches.
Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
Compare performance and cost trade-offs of PEFT methods vs. full fine-tuning.
Deploy and scale fine-tuned LLMs with reduced compute and storage requirements.
This instructor-led, live training in Quito (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 in Quito (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to apply RLHF to fine-tune large AI models for superior performance, safety, and alignment.By the end of this training, participants will be able to:
Understand the theoretical foundations of RLHF and why it is essential in modern AI development.
Implement reward models based on human feedback to guide reinforcement learning processes.
Fine-tune large language models using RLHF techniques to align outputs with human preferences.
Apply best practices for scaling RLHF workflows for production-grade AI systems.
This instructor-led, live training in Quito (online or onsite) is aimed at 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:
Understand the fundamentals of fine-tuning for finance applications.
Leverage pre-trained models for domain-specific tasks in finance.
Apply techniques for fraud detection, risk assessment, and financial advice generation.
Ensure compliance with financial regulations such as GDPR and SOX.
Implement data security and ethical AI practices in financial applications.
This instructor-led, live training in Quito (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 Quito (online or onsite) is aimed at advanced-level 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:
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 Quito (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 Quito (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 Quito (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 Quito (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 Quito (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 Quito (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.
This instructor-led, live training in Quito (online or onsite) 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 Quito (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 Quito (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.
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