Optimizing Large Models for Cost-Effective Fine-Tuning Training Course
Optimizing large models for fine-tuning is essential to make advanced AI applications practical and budget-friendly. This course covers strategies for cutting computational costs, such as distributed training, model quantization, and hardware optimization, empowering participants to deploy and fine-tune large models efficiently.
This instructor-led, live training (available online or at your venue) is designed for advanced professionals seeking to master techniques for optimizing large models for cost-effective fine-tuning in real-world business contexts.
Upon completing 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 boost efficiency.
- Maximize hardware utilization for fine-tuning operations.
- Effectively deploy fine-tuned models within production environments.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation in a live-lab environment.
Customization Options
- To arrange a customized training session for this course, please get in touch with us.
Course Outline
Introduction to Optimizing Large Models
- Overview of large model architectures
- Challenges in fine-tuning large models
- Importance of cost-effective optimization
Distributed Training Techniques
- Introduction to data and model parallelism
- Frameworks for distributed training: PyTorch and TensorFlow
- Scaling across multiple GPUs and nodes
Model Quantization and Pruning
- Understanding quantization techniques
- Applying pruning to reduce model size
- Trade-offs between accuracy and efficiency
Hardware Optimization
- Choosing the right hardware for fine-tuning tasks
- Optimizing GPU and TPU utilization
- Using specialized accelerators for large models
Efficient Data Management
- Strategies for managing large datasets
- Preprocessing and batching for performance
- Data augmentation techniques
Deploying Optimized Models
- Techniques for deploying fine-tuned models
- Monitoring and maintaining model performance
- Real-world examples of optimized model deployment
Advanced Optimization Techniques
- Exploring low-rank adaptation (LoRA)
- Using adapters for modular fine-tuning
- Future trends in model optimization
Summary and Next Steps
Requirements
- Experience with deep learning frameworks such as PyTorch or TensorFlow
- Familiarity with large language models and their practical applications
- Understanding of distributed computing concepts
Target Audience
- Machine learning engineers
- Cloud AI specialists
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Optimizing Large Models for Cost-Effective Fine-Tuning Training Course - Enquiry
Related Courses
Advanced Fine-Tuning & Prompt Management in Vertex AI
14 HoursVertex AI offers sophisticated tools for fine-tuning large language models and managing prompts, empowering developers and data teams to enhance model accuracy, streamline iteration workflows, and ensure rigorous evaluation through built-in libraries and services.
This instructor-led live training, available both online and onsite, is designed for intermediate to advanced practitioners who aim to improve the performance and reliability of generative AI applications by leveraging supervised fine-tuning, prompt versioning, and evaluation services within Vertex AI.
Upon completing this training, participants will be able to:
- Apply supervised fine-tuning techniques to Gemini models in Vertex AI.
- Implement prompt management workflows, including versioning and testing.
- Leverage evaluation libraries to benchmark and optimize AI performance.
- Deploy and monitor improved models in production environments.
Format of the Course
- Interactive lectures and discussions.
- Hands-on labs focused on Vertex AI fine-tuning and prompt tools.
- Case studies demonstrating enterprise model optimization.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
Advanced Techniques in Transfer Learning
14 HoursDelivered by an instructor in a live format (available online or onsite), this programme is designed for seasoned machine learning professionals aiming to master state-of-the-art transfer learning techniques and apply them to intricate real-world scenarios.
Upon completion of this training, participants will be equipped to:
- Grasp advanced concepts and methodologies in transfer learning.
- Deploy domain-specific adaptation techniques for pre-trained models.
- Utilise continual learning to handle evolving tasks and datasets.
- Master multi-task fine-tuning to boost model performance across various tasks.
Continual Learning and Model Update Strategies for Fine-Tuned Models
14 HoursThis instructor-led, live training in Kenya (online or onsite) targets advanced AI maintenance engineers and MLOps professionals who aim to establish resilient ongoing learning pipelines and effective revision strategies for deployed, fine-tuned models.
Upon completing this training, participants will be capable of:
- Designing and executing ongoing learning workflows for deployed models.
- Reducing catastrophic forgetting through appropriate training and memory management techniques.
- Automating monitoring and update triggers in response to model drift or data changes.
- Incorporating model revision strategies into existing CI/CD and MLOps pipelines.
Deploying Fine-Tuned Models in Production
21 HoursThis instructor-led, live training in Kenya (online or on-site) is designed for advanced professionals seeking to deploy fine-tuned models reliably and efficiently.
Upon completion 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.
Domain-Specific Fine-Tuning for Finance
21 HoursThis instructor-led live training Kenya (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.
Fine-Tuning Models and Large Language Models (LLMs)
14 HoursThis instructor-led, live training in Kenya (online or onsite) is designed for intermediate to advanced-level professionals seeking 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 model refinement and its real-world applications.
- Prepare datasets effectively for refining pre-trained models.
- Refine Large Language Models (LLMs) for Natural Language Processing (NLP) tasks.
- Optimize model performance and navigate common challenges.
Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)
14 HoursThis instructor-led live training in Kenya (online or onsite) is designed for intermediate developers and AI practitioners aiming to implement fine-tuning strategies for large models without the need for extensive computational resources.
Upon completing this training, participants will be equipped to:
- Comprehend the foundational principles of Low-Rank Adaptation (LoRA).
- Apply LoRA for the efficient fine-tuning of large models.
- Enhance fine-tuning processes for resource-constrained settings.
- Assess and deploy LoRA-optimized models for real-world applications.
Fine-Tuning Multimodal Models
28 HoursThis instructor-led, live training in Kenya (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.
Fine-Tuning for Natural Language Processing (NLP)
21 HoursThis instructor-led, live training in Kenya (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:
- 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.
Fine-Tuning AI for Financial Services: Risk Prediction and Fraud Detection
14 HoursThis instructor-led, live training in Kenya (online or onsite) is designed for advanced data scientists and AI engineers in the financial sector who want to fine-tune models for applications like credit scoring, fraud detection, and risk modelling using domain-specific financial data.
Upon completion of this training, participants will be able to:
- Fine-tune AI models on financial datasets to enhance fraud and risk prediction.
- Utilise techniques such as transfer learning, LoRA, and regularisation to improve model efficiency.
- Incorporate financial compliance requirements into the AI modelling workflow.
- Deploy fine-tuned models for production use within financial services platforms.
Fine-Tuning AI for Healthcare: Medical Diagnosis and Predictive Analytics
14 HoursThis instructor-led, live training in Kenya (online or onsite) is designed for medical AI developers and data scientists at intermediate to advanced levels who wish to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.
Upon completing this training, participants will be able to:
- Fine-tune AI models using healthcare datasets such as EMRs, imaging, and time-series data.
- Implement transfer learning, domain adaptation, and model compression within medical contexts.
- Manage privacy concerns, bias, and regulatory compliance during model development.
- Deploy and monitor fine-tuned models in real-world healthcare settings.
Fine-Tuning DeepSeek LLM for Custom AI Models
21 HoursThis instructor-led, live training session, available online or onsite, is designed for advanced AI researchers, machine learning engineers, and developers aiming to fine-tune DeepSeek LLM models. The objective is to create specialized AI applications tailored to specific industries, domains, or business needs.
Upon completion of this training, participants will be able to:
- Comprehend the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3.
- Prepare and preprocess datasets for the fine-tuning process.
- Fine-tune DeepSeek LLM for applications targeted at specific domains.
- Optimize and deploy fine-tuned models with efficiency.
Fine-Tuning Defense AI for Autonomous Systems and Surveillance
14 HoursThis guided, live training in Kenya (online or onsite) is designed for advanced-level defense AI engineers and military technology developers who want to fine-tune deep learning models for application in autonomous vehicles, drones, and surveillance systems, while adhering to strict security and reliability standards.
Upon completion of this training, participants will be able to:
- Fine-tune computer vision and sensor fusion models for surveillance and targeting operations.
- Adjust autonomous AI systems to cope with changing environments and mission requirements.
- Implement robust validation and fail-safe mechanisms within model pipelines.
- Ensure alignment with defense-specific compliance, safety, and security standards.
Fine-Tuning Legal AI Models: Contract Review and Legal Research
14 HoursThis instructor-led, live training in Kenya (online or onsite) 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.
Fine-Tuning Large Language Models Using QLoRA
14 HoursThis guided, live instruction Kenya (available online or onsite) is tailored for intermediate to advanced-level machine learning engineers, AI developers, and data scientists who seek 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:
- Comprehend the theory underpinning QLoRA and quantization techniques for LLMs.
- Execute QLoRA for refining large language models in domain-specific contexts.
- Refine tuning performance on limited computational resources using quantization.
- Deploy and evaluate fine-tuned models in real-world applications efficiently.
