Delivered either online or on-site, our instructor-led live Fine-Tuning training courses offer interactive, hands-on sessions that demonstrate how to leverage customized machine learning models to enhance performance for specific tasks, datasets, or applications.
Fine-Tuning training is offered as "online live training" or "onsite live training". Online live training, also referred to as "remote live training", is facilitated via an interactive remote desktop. Onsite live training can be conducted locally at customer premises in Durban or within NobleProg corporate training centers in Durban.
NobleProg -- Your Local Training Provider
Garden Court South Beach
Durban, South Africa
Way to get there
From Durban city centre: follow the beachfront/Marine Parade south toward South Beach.
By car/taxi: give the driver the address 73 O.R. Tambo Parade, South Beach, Durban 4001.
From King Shaka International Airport: the hotel is roughly 30 km ;from the airport.
This instructor-led, live training in Durban (online or onsite) is designed for advanced defence AI engineers and military technology developers who wish to fine-tune deep learning models for use in autonomous vehicles, drones, and surveillance systems, whilst meeting stringent security and reliability standards.
Upon completion of this training, participants will be equipped to:
Fine-tune computer vision and sensor fusion models for surveillance and targeting operations.
Adapt autonomous AI systems to dynamic environments and varying mission profiles.
Implement robust validation and fail-safe mechanisms within model pipelines.
Ensure strict alignment with defence-specific compliance, safety, and security standards.
This instructor-led, live training in Durban (online or onsite) is designed for intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks such as contract analysis, clause extraction, and automated legal research within legal service environments.
Upon completion of this training, participants will be able to:
Prepare and cleanse legal documents for the fine-tuning of NLP models.
Apply fine-tuning strategies to enhance 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 Durban (online or onsite) targets intermediate to advanced medical AI developers and data scientists who wish 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 able to:
Fine-tune AI models on healthcare datasets, including EMRs, imaging data, and time-series data.
Apply transfer learning, domain adaptation, and model compression techniques within medical contexts.
Address privacy concerns, bias, and regulatory compliance during model development.
Deploy and monitor fine-tuned models in real-world healthcare settings.
This instructor-led, live training in Durban (online or onsite) is aimed at advanced-level data scientists and AI engineers in the financial sector who wish to refine models for applications such as credit scoring, fraud detection, and risk modelling using domain-specific financial data.
By the end of this training, participants will be able to:
Refine AI models on financial datasets for improved fraud and risk prediction.
Apply techniques such as transfer learning, LoRA, and regularisation to enhance model efficiency.
Integrate financial compliance considerations into the AI modelling workflow.
Deploy refined models for production use in financial services platforms.
This instructor-led, live training in Durban (online or onsite) is aimed at advanced-level AI maintenance engineers and MLOps professionals who wish 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 Durban (online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimise 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 Durban (online or onsite) is targeted 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:
Grasp 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, conducted Durban (online or onsite), is tailored for 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 Durban (online or onsite) is designed for intermediate-level NLP engineers and knowledge management teams seeking to fine-tune RAG pipelines to enhance performance in question answering, enterprise search, and summarisation use cases.
By the end of this training, participants will be able to:
Grasp 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 Durban (online or onsite) is aimed at intermediate-level ML practitioners and AI developers who wish to fine-tune and deploy open-weight models such as LLaMA, Mistral, and Qwen for specific business or internal applications.
By the end of this training, participants will be able to:
Understand the ecosystem and differences between 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 live, instructor-led training in Durban (delivered online or on-site) targets intermediate data scientists and AI engineers who aim to fine-tune large language models with improved cost-effectiveness and efficiency, employing methods such as LoRA, Adapter Tuning, and Prefix Tuning.
Upon completion, participants will be able to:
Comprehend the theory supporting parameter-efficient fine-tuning techniques.
Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT.
Evaluate the trade-offs between performance and cost when comparing PEFT methods against full fine-tuning.
Deploy and scale fine-tuned LLMs while significantly reducing compute and storage overheads.
This instructor-led, live training in Durban (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 customisations.
By the end of this training, participants will be able to:
Understand the theory behind QLoRA and quantisation techniques for LLMs.
Implement QLoRA in fine-tuning large language models for domain-specific applications.
Optimise fine-tuning performance on limited computational resources using quantisation.
Deploy and evaluate fine-tuned models in real-world applications efficiently.
This instructor-led, live training in Durban (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 Durban (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 Durban (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 Durban (online or on-site) is aimed at advanced-level professionals who wish to master techniques for optimizing large models for cost-efficient 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 Durban (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimise 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.
Optimise LLM performance for practical applications.
This instructor-led, live training in Durban (online or in-person) is designed for advanced professionals who want to master multimodal model refinement for innovative AI solutions.
Upon completing this training, participants will be able to:
Comprehend the architecture of multimodal models such as CLIP and Flamingo.
Effectively prepare and preprocess multimodal datasets.
Refine multimodal models for specific tasks.
Optimize models for real-world applications and performance.
This instructor-led, live training in Durban (online or onsite) is designed for advanced-level AI researchers, machine learning engineers, and developers who wish to fine-tune DeepSeek LLM models to create specialised 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.
Optimise and deploy fine-tuned models efficiently.
This instructor-led, live training in Durban (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 Durban (online or onsite) targets 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 training in Durban explores advanced fine-tuning and prompt management in Vertex AI, assisting intermediate and advanced practitioners in optimising Gemini models, implementing rigorous evaluation workflows, and deploying reliable generative AI solutions for real-world enterprise settings.
This instructor-led, live training in Durban (online or onsite) is designed for beginner to intermediate 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 Durban (online or on-site) is designed for 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 conclusion 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.
Optimise fine-tuning for resource-constrained environments.
Evaluate and deploy LoRA-tuned models for practical applications.
This instructor-led, live training in Durban (online or onsite) is designed for intermediate to advanced professionals who wish to tailor pre-trained models for specific tasks and datasets.
By the end of this training, participants will be able to:
Grasp the principles of customisation and its applications.
Prepare datasets for customising pre-trained models.
Customise large language models (LLMs) for NLP tasks.
Optimise model performance and address common challenges.
This instructor-led live training in Durban (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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