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 Nepal or in NobleProg corporate training centers in Nepal.
NobleProg -- Your Local Training Provider
Nepal, Kathmandu - Classroom
near Soaltee, Tahachal Marg, Kathmandu, Nepal, 44600
Set in Kathmandu, this classroom is well located near Tahachal Marg with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
Nepal, Thamel, KTM - Classroom
near Radisson , Ward 2, Kathmandu, Nepal, 44600
Set in Kathmandu, this classroom is well located near Thamel, with all amenities and WiFi.
For Sales Enquires and Meetings
All our centres have batches running on weekdays and weekends hence, please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate offices.
This instructor-led, live training in Nepal (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 Nepal (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 Nepal (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 Nepal (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 Nepal (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 Nepal (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.
This instructor-led live training (available online or onsite) is designed for advanced computer vision engineers and AI developers who wish to fine-tune VLMs like CLIP and Flamingo to enhance 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 Nepal, offered online or onsite, targets intermediate-level ML engineers and AI compliance professionals seeking 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 Nepal (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 Nepal (online or onsite) is designed for 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.
Upon completion of this training, participants will be able to:
Grasp the ecosystem and key distinctions among 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 instructor-led, live training in Nepal (available online or on-site) is designed for intermediate-level data scientists and AI engineers who wish to fine-tune large language models more affordably and efficiently using techniques like LoRA, Adapter Tuning, and Prefix Tuning.
Upon completing 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 the Hugging Face PEFT library.
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 Nepal (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 Nepal (online or onsite) is targeted at advanced 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 Nepal (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 Nepal (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.
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 Nepal (online or onsite) is designed for advanced professionals aiming to master techniques for optimizing large models to achieve cost-effective fine-tuning in real-world scenarios.
Upon completion of this training, participants will be able to:
Grasp 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 Nepal (online or onsite) is designed for intermediate-level professionals who aim to utilise the power of prompt engineering and few-shot learning to optimise LLM performance for real-world applications.
Upon completing this training, participants will be capable of:
Grasping the core principles of prompt engineering and few-shot learning.
Crafting effective prompts suited for various NLP tasks.
Utilising few-shot techniques to adapt LLMs with minimal data requirements.
Optimising LLM performance for practical, everyday applications.
This instructor-led, live training in Nepal (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 Nepal (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 Nepal (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 Nepal (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 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 Nepal (online or onsite) is designed for machine learning professionals at beginner to intermediate levels who aim to understand and apply transfer learning techniques to enhance efficiency and performance in AI projects.
By the conclusion of this training, participants will be able to:
Comprehend the fundamental 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 Nepal (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 Nepal (online or onsite) is designed for intermediate to advanced professionals who want to customize pre-trained models for specific tasks and datasets.
By the end of this training, participants will be able to:
Grasp 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 Nepal (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.
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