AI Model Compression Training Course

AI Model Compression Training Course

Introduction

AI Model Compression Training Course is designed to equip professionals with advanced skills in deep learning optimization, neural network compression, efficient AI deployment, and edge intelligence. As organizations increasingly adopt Artificial Intelligence (AI), Machine Learning (ML), Generative AI, and Edge AI solutions, reducing model size, improving inference speed, and lowering computational costs have become critical capabilities. This course explores modern AI model compression techniques including quantization, pruning, knowledge distillation, neural architecture search (NAS), low-rank optimization, and hardware-aware AI acceleration to create high-performance, lightweight AI systems.

Participants will gain practical expertise in transforming large-scale AI models into optimized solutions suitable for cloud platforms, mobile devices, IoT systems, autonomous applications, and embedded environments. Through hands-on labs and industry case studies, learners will understand how leading organizations apply efficient AI engineering, model optimization frameworks, and deployment strategies to achieve scalable, cost-effective, and sustainable AI solutions.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand the fundamentals of AI model compression and efficient deep learning architectures. 
2. Apply neural network pruning techniques to reduce model complexity. 
3. Implement model quantization strategies for faster AI inference. 
4. Design optimized edge AI and embedded machine learning solutions. 
5. Use knowledge distillation frameworks to transfer intelligence from large models to smaller models. 
6. Optimize Large Language Models (LLMs) for resource-efficient deployment. 
7. Apply hardware-aware AI optimization techniques for GPUs, CPUs, and accelerators. 
8. Analyze AI model performance using accuracy, latency, memory, and energy metrics. 
9. Implement TensorFlow, PyTorch, ONNX, and AI optimization toolchains. 
10. Develop scalable AI deployment pipelines with compressed models. 
11. Explore automated model compression using Neural Architecture Search (NAS). 
12. Improve AI sustainability through green AI and computational efficiency practices. 
13. Build production-ready compressed AI models for real-world applications. 

Target Audience

1. AI Engineers and Machine Learning Engineers 
2. Data Scientists and Data Analysts 
3. Deep Learning Researchers 
4. Software Engineers developing AI applications 
5. MLOps Engineers and AI Platform Engineers 
6. Edge Computing and IoT Developers 
7. Cloud AI Solution Architects 
8. Technology Professionals transitioning into AI Engineering 

Course Modules

Module 1: Fundamentals of AI Model Compression

• Introduction to AI efficiency and model optimization principles
• Understanding model size, latency, memory, and computational costs 
• Challenges of deploying large AI models in production environments 
• Overview of compression methods and optimization workflows 
• AI compression lifecycle from development to deployment 
• Case Study: Optimizing a computer vision model for deployment on a low-power industrial IoT camera.

Module 2: Neural Network Pruning Techniques

• Understanding structured and unstructured pruning methods 
• Weight pruning and sparsity optimization strategies 
• Channel pruning and filter reduction techniques 
• Implementing pruning using TensorFlow Model Optimization Toolkit 
• Measuring accuracy impact after compression 
• Case Study: Reducing the size of a medical image classification model while maintaining diagnostic accuracy.

Module 3: AI Model Quantization

• Fundamentals of numerical precision reduction 
• Float32, Float16, and INT8 quantization methods 
• Post-training quantization and quantization-aware training 
• Optimizing models for mobile and edge hardware 
• Using TensorFlow Lite and ONNX Runtime quantization tools 
• Case Study: Deploying a real-time object detection model on a smartphone using INT8 optimization.

Module 4: Knowledge Distillation for Efficient AI

• Teacher-student model architectures 
• Knowledge transfer techniques in deep learning 
• Feature-based and response-based distillation 
• Compressing large AI models into lightweight versions 
• Improving small model performance through distillation 
• Case Study: Creating a lightweight NLP model from a large transformer model for customer support automation.

Module 5: Large Language Model (LLM) Compression

• Challenges of deploying large language models 
• LLM quantization and parameter reduction 
• Low-rank adaptation and matrix factorization 
• Efficient transformer architectures 
• Optimizing Generative AI models for enterprise use 
• Case Study: Compressing an enterprise chatbot LLM for faster deployment on limited cloud infrastructure.

Module 6: Neural Architecture Search and Automated Optimization

• Introduction to Neural Architecture Search (NAS) 
• Automated model design and optimization 
• Hardware-aware architecture selection 
• Efficient convolutional neural networks 
• AutoML approaches for compression 
• Case Study: Using NAS techniques to create an optimized vision model for autonomous vehicles.

Module 7: AI Hardware Acceleration and Deployment

• Optimizing AI models for GPUs, TPUs, and NPUs 
• Edge AI deployment strategies 
• Model conversion using ONNX and TensorRT 
• AI inference optimization techniques 
• Managing latency and energy consumption 
• Case Study: Deploying a compressed AI surveillance model using NVIDIA edge computing hardware.

Module 8: Production AI Optimization and MLOps

• Building AI compression pipelines 
• Monitoring compressed model performance 
• Model versioning and lifecycle management 
• Continuous optimization in production environments 
• Implementing efficient AI deployment practices 
• Case Study: Creating an enterprise MLOps pipeline for maintaining optimized AI recommendation models.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.