Home
Artificial Intelligence (AI) Training
Edge AI Training
Fine-Tuning Lightweight Models for Edge AI Deployment Training Course

Fine-Tuning Lightweight Models for Edge AI Deployment Training Course

Model fine-tuning involves adapting pre-trained models to specific tasks or environments.

This instructor-led, live training (available online or on-site) 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 in government settings.

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.

Format of the Course

Interactive lecture and discussion.
Extensive exercises and practice sessions.
Hands-on implementation in a live-lab environment.

Course Customization Options

To request a customized training for government, please contact us to arrange.

This course is available as onsite live training in US Government or online live training.

Thank you for sending your enquiry! One of our team members will contact you shortly.

Thank you for sending your booking! One of our team members will contact you shortly.

Course Outline

Introduction to Edge AI and Model Optimization for Government

Understanding edge computing and AI workloads in government applications
Balancing performance with resource constraints in public sector environments
Overview of model optimization strategies for government use cases

Model Selection and Pre-training for Government

Selecting lightweight models suitable for edge devices, such as MobileNet, TinyML, and SqueezeNet
Evaluating model architectures that meet the specific requirements of government edge computing
Leveraging pre-trained models to accelerate development cycles in public sector projects

Fine-Tuning and Transfer Learning for Government

Principles of transfer learning and their application in government AI initiatives
Adapting pre-trained models to custom datasets relevant to public sector operations
Implementing practical fine-tuning workflows for government-specific tasks

Model Quantization for Government

Post-training quantization techniques to enhance efficiency in government edge devices
Quantization-aware training methods for improved performance and accuracy
Evaluating the trade-offs between model size and performance in public sector applications

Model Pruning and Compression for Government

Pruning strategies, including structured and unstructured approaches, to optimize government models
Techniques for compression and weight sharing to reduce model size without compromising accuracy
Benchmarking compressed models to ensure they meet the performance requirements of government operations

Deployment Frameworks and Tools for Government

Utilizing frameworks such as TensorFlow Lite, PyTorch Mobile, and ONNX for government deployments
Ensuring edge hardware compatibility and runtime environments in government IT infrastructure
Employing toolchains to facilitate cross-platform deployment in public sector projects

Hands-On Deployment for Government

Deploying AI models to government edge devices, including Raspberry Pi, Jetson Nano, and mobile devices
Conducting profiling and benchmarking to optimize performance in government environments
Addressing deployment issues to ensure reliable operation of AI systems in public sector settings

Summary and Next Steps for Government

Requirements

An understanding of machine learning fundamentals for government applications.
Experience with Python and deep learning frameworks.
Familiarity with embedded systems or edge device constraints.

Audience

Embedded AI developers for government projects.
Edge computing specialists for government initiatives.
Machine learning engineers focusing on edge deployment for government use cases.

14 Hours

Number of participants

Online

Classroom

Select Location

Please select a Venue

Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

Fine-Tuning Lightweight Models for Edge AI Deployment Training Course - Booking

Full Name *

Email *

Phone *

Job Title

Company Name

Address 1 *

City *

State / Province

Country *

Postcode *

Start Date

Tax ID

Dates are subject to availability and take place between 09:30 and 16:30.

Payment *

Bank Transfer (Invoice, PO)

Debit / Credit Card

Comments

Terms and Conditions *

I am an authorised representative of the above named client and I wish to book the above courses or services in accordance with NobleProg Terms and Conditions and Privacy Policy.

Inform me about discounts and promotions

Please read our Privacy Policy to find out how we use your data

Fine-Tuning Lightweight Models for Edge AI Deployment Training Course - Enquiry

Full Name *

Email *

Phone *

Number of participants

Company Name

Company Address

How do you want to take the course?

Client Premises

Online

Classroom

State / Province *

Comments

Inform me about discounts and promotions

Please read our Privacy Policy to find out how we use your data

Fine-Tuning Lightweight Models for Edge AI Deployment - Consultancy Enquiry

Consultancy Enquiry

Full Name *

Phone *

Email *

Company Name

State / Province *

Consultancy Subject *

Consultancy Goal

Who will the consultant work with?

Consultancy Urgency *

Comments

Inform me about discounts and promotions

Please read our Privacy Policy to find out how we use your data

Upcoming Courses

Fine-Tuning Lightweight Models for Edge AI Deployment

2026-02-02 09:30

14 hours

VA, Richmond - Two Paragon Place

$ 2703 (Online)

$ 3903 (Classroom)

Fine-Tuning Lightweight Models for Edge AI Deployment

2026-02-16 09:30

14 hours

VA, Reston - Sunrise Valley

$ 2703 (Online)

$ 3903 (Classroom)

Fine-Tuning Lightweight Models for Edge AI Deployment

2026-03-02 09:30

14 hours

VA, Reston - Reston Town Center I

$ 2703 (Online)

$ 3903 (Classroom)

Fine-Tuning Lightweight Models for Edge AI Deployment

2026-03-16 09:30

14 hours

VA, Richmond - Sun Trust Center Downtown

$ 2703 (Online)

$ 3903 (Classroom)

Related Courses

Advanced Techniques in Transfer Learning

14 Hours

This instructor-led, live training (available online or onsite) is designed for advanced-level machine learning professionals who seek to master cutting-edge transfer learning techniques and apply them to complex real-world challenges. 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 strategies to manage evolving tasks and datasets. - Master multi-task fine-tuning to improve model performance across various tasks. This training is tailored to enhance the skills of professionals working in technical roles, ensuring they are equipped with the knowledge necessary to advance their capabilities for government and private sector applications.

Deploying Fine-Tuned Models in Production

21 Hours

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently for government applications. By the end of this training, participants will be able to: - Understand the challenges associated with deploying fine-tuned models into production environments. - Containerize and deploy models using tools such as Docker and Kubernetes. - Implement monitoring and logging mechanisms for deployed models to ensure ongoing performance and compliance. - Optimize models for latency and scalability in real-world scenarios, aligning with public sector workflows and governance requirements.

Domain-Specific Fine-Tuning for Finance

21 Hours

This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who wish to develop practical skills in customizing artificial intelligence models for critical financial tasks. By the end of this training, participants will be able to: - Understand the foundational principles of fine-tuning AI models for finance applications. - Utilize pre-trained models for domain-specific tasks within the financial sector. - Apply techniques for fraud detection, risk assessment, and the generation of financial advice. - Ensure compliance with financial regulations such as GDPR and SOX. - Implement robust data security and ethical AI practices in financial applications. This training is tailored to align with the needs and standards required for government agencies, ensuring that participants are well-equipped to handle sensitive financial information and adhere to regulatory requirements.

Fine-Tuning Models and Large Language Models (LLMs)

14 Hours

This instructor-led, live training in US (online or onsite) is designed for intermediate to advanced professionals who seek to customize pre-trained models for specific tasks and datasets. By the end of this training, participants will be able to: - Comprehend the principles of fine-tuning and their applications. - Prepare datasets for the fine-tuning of pre-trained models. - Fine-tune large language models (LLMs) for natural language processing (NLP) tasks. - Enhance model performance and address common challenges. This training is tailored to meet the needs of professionals working in various sectors, including those for government, ensuring that participants are well-equipped to apply these skills in their respective fields.

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

14 Hours

This instructor-led, live training (online or onsite) is designed for intermediate-level developers and AI practitioners who aim to implement fine-tuning strategies for large models with minimal computational resources. By the end of this training, participants will be able to: - Understand the principles of Low-Rank Adaptation (LoRA). - Implement LoRA to efficiently fine-tune large models. - Optimize fine-tuning processes for environments with limited resources. - Evaluate and deploy LoRA-tuned models for practical applications, ensuring alignment with public sector workflows and governance for government.

Fine-Tuning Multimodal Models

28 Hours

This instructor-led, live training in [location] (online or onsite) is aimed at advanced-level professionals who wish to master multimodal model fine-tuning for innovative AI solutions for government. By the end of this training, participants will be able to: - Understand the architecture of multimodal models such as 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 Hours

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who aim to enhance their natural language processing (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 core principles of fine-tuning for NLP tasks. - Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications relevant to their work. - Optimize hyperparameters to achieve enhanced model performance. - Evaluate and deploy fine-tuned models in real-world scenarios, ensuring alignment with public sector workflows and governance standards for government.

Fine-Tuning DeepSeek LLM for Custom AI Models

21 Hours

This instructor-led, live training (available online or onsite) is designed for advanced-level AI researchers, machine learning engineers, and developers who aim to fine-tune DeepSeek LLM models to develop 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 models for domain-specific applications. - Optimize and deploy fine-tuned models efficiently. This training is tailored to enhance the skills necessary for government agencies to leverage advanced AI technologies effectively, ensuring alignment with public sector workflows, governance, and accountability.

Fine-Tuning Large Language Models Using QLoRA

14 Hours

This instructor-led, live training (online or onsite) is aimed at 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 customizations. By the end of this training, participants will be able to: - Understand the theory behind QLoRA and quantization techniques for large language models. - Implement QLoRA in fine-tuning large language models for domain-specific applications. - Optimize fine-tuning performance on limited computational resources using quantization methods. - Deploy and evaluate fine-tuned models efficiently in real-world scenarios, ensuring alignment with public sector workflows and governance standards for government.

Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

14 Hours

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to apply Reinforcement Learning from Human Feedback (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 its importance 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 to support production-grade AI systems for government.

Optimizing Large Models for Cost-Effective Fine-Tuning

21 Hours

This instructor-led, live training (offered online or on-site) is designed for advanced-level professionals who aim to master techniques for optimizing large models for cost-effective fine-tuning in real-world applications. By the end of this training, participants will be able to: - Understand the challenges associated with fine-tuning large models. - Apply distributed training methods to enhance the performance of large models. - Utilize model quantization and pruning to improve efficiency. - Optimize hardware usage for fine-tuning tasks. - Effectively deploy fine-tuned models in production environments, ensuring alignment with public sector workflows and governance standards for government.

Prompt Engineering and Few-Shot Fine-Tuning

14 Hours

This instructor-led, live training (conducted online or onsite) is designed for intermediate-level professionals who aim to harness the capabilities of prompt engineering and few-shot learning to enhance the performance of Large Language Models (LLMs) for real-world applications. By the end of this training, participants will be able to: - Understand the foundational principles of prompt engineering and few-shot learning. - Develop effective prompts for a variety of natural language processing tasks. - Utilize few-shot techniques to adapt LLMs with minimal data requirements. - Optimize LLM performance to meet practical needs, ensuring alignment with public sector workflows and governance standards for government.

Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs

14 Hours

This instructor-led, live training (online or onsite) is designed for intermediate-level data scientists and AI engineers who aim to refine large language models more cost-effectively and efficiently using techniques such as LoRA, Adapter Tuning, and Prefix Tuning. By the end of this training, participants will be able to: - Understand the theoretical foundations of parameter-efficient fine-tuning methods. - Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT for government applications. - Evaluate the performance and cost trade-offs of PEFT methods compared to full fine-tuning. - Deploy and scale fine-tuned LLMs with minimized compute and storage requirements.

Introduction to Transfer Learning

14 Hours

This instructor-led, live training (online or onsite) is designed for government machine learning professionals at the beginner to intermediate level who wish to understand and apply transfer learning techniques to enhance efficiency and performance in AI projects for government. By the end of this training, participants will be able to: - Comprehend the fundamental concepts and advantages of transfer learning. - Examine widely used pre-trained models and their applications. - Conduct fine-tuning of pre-trained models for specific tasks. - Utilize transfer learning to address real-world challenges in natural language processing (NLP) and computer vision.

Troubleshooting Fine-Tuning Challenges

14 Hours

This instructor-led, live training in US (online or onsite) is designed for advanced-level professionals who wish to enhance their skills in diagnosing and addressing fine-tuning challenges for machine learning models. By the end of this training, participants will be able to: - Identify issues such as overfitting, underfitting, and data imbalance. - Implement strategies to improve model convergence. - Optimize fine-tuning pipelines for enhanced performance. - Debug training processes using practical tools and techniques, ensuring effective solutions for government applications.

Fine-Tuning Lightweight Models for Edge AI Deployment Training Course

Course Outline

Requirements

Upcoming Courses

Fine-Tuning Lightweight Models for Edge AI Deployment

Fine-Tuning Lightweight Models for Edge AI Deployment

Fine-Tuning Lightweight Models for Edge AI Deployment

Fine-Tuning Lightweight Models for Edge AI Deployment

Related Courses

Advanced Techniques in Transfer Learning

Deploying Fine-Tuned Models in Production

Domain-Specific Fine-Tuning for Finance

Fine-Tuning Models and Large Language Models (LLMs)

Efficient Fine-Tuning with Low-Rank Adaptation (LoRA)

Fine-Tuning Multimodal Models

Fine-Tuning for Natural Language Processing (NLP)

Fine-Tuning DeepSeek LLM for Custom AI Models

Fine-Tuning Large Language Models Using QLoRA

Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

Optimizing Large Models for Cost-Effective Fine-Tuning

Prompt Engineering and Few-Shot Fine-Tuning

Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs

Introduction to Transfer Learning

Troubleshooting Fine-Tuning Challenges

Related Categories

Edge AI

Fine-Tuning