AI Inference Optimization Training Course

AI Inference Optimization Training Course

Introduction

AI Inference Optimization Training Course is designed to equip professionals with advanced skills in Artificial Intelligence (AI) deployment, machine learning inference acceleration, deep learning optimization, model performance tuning, and scalable AI systems engineering. As organizations increasingly adopt generative AI, large language models (LLMs), computer vision, natural language processing (NLP), and edge AI applications, optimizing inference pipelines has become critical for achieving low latency, high throughput, cost efficiency, and real-time intelligence. This course explores modern techniques including model quantization, pruning, knowledge distillation, hardware acceleration, inference engines, GPU optimization, cloud AI deployment, and AI workload optimization.

Participants will gain practical expertise in designing and improving production-ready AI inference systems using cutting-edge technologies such as TensorRT, ONNX Runtime, OpenVINO, CUDA optimization, TPU acceleration, edge computing frameworks, and AI infrastructure platforms. Through hands-on labs and real-world case studies, learners will understand how leading organizations optimize AI models for speed, scalability, energy efficiency, and reliability across cloud, enterprise, mobile, and embedded environments. The course prepares professionals to build next-generation high-performance AI applications that deliver faster predictions and improved user experiences.

Course Duration

5 days

Course Objectives

By completing this course, participants will be able to:

1. Understand the fundamentals of AI inference architecture and optimization strategies. 
2. Apply model compression techniques including quantization, pruning, and distillation. 
3. Optimize deep learning models for production deployment. 
4. Improve AI application performance through latency reduction and throughput enhancement. 
5. Implement GPU acceleration and hardware-aware AI optimization. 
6. Deploy optimized models using TensorRT, ONNX Runtime, and OpenVINO frameworks. 
7. Design scalable cloud-based and edge AI inference pipelines. 
8. Apply LLM inference optimization techniques for generative AI workloads. 
9. Optimize memory usage through efficient model serving and resource management. 
10. Implement batching, caching, and parallel inference strategies. 
11. Evaluate AI systems using performance benchmarking and profiling tools. 
12. Build cost-efficient AI infrastructure using AI optimization best practices. 
13. Develop production-ready AI solutions using MLOps and automated optimization workflows. 

Target Audience

1. AI Engineers and Machine Learning Engineers 
2. Data Scientists and Deep Learning Specialists 
3. Software Engineers developing AI applications 
4. MLOps Engineers and AI Platform Developers 
5. Cloud Engineers managing AI workloads 
6. Research Scientists working on AI performance improvement 
7. Embedded Systems and Edge AI Developers 
8. Technology Architects designing AI solutions 

Course Modules

Module 1: Fundamentals of AI Inference Optimization

• Understanding AI inference workflows and deployment challenges 
• Difference between training optimization and inference optimization 
• AI model execution lifecycle and performance bottlenecks 
• Measuring latency, throughput, memory, and computational efficiency 
• Introduction to inference optimization frameworks 
• Case Study: Optimizing a customer service chatbot model to reduce response latency in a production environment.

Module 2: Neural Network Model Compression Techniques

• Introduction to model compression strategies 
• Neural network pruning methodologies 
• Weight and activation quantization techniques 
• Knowledge distillation for lightweight AI models 
• Trade-offs between accuracy and performance optimization 
• Case Study: Compressing an image recognition model for deployment on mobile devices.

Module 3: GPU and Hardware Acceleration Optimization

• Understanding GPU-based AI inference acceleration 
• CUDA optimization fundamentals 
• Tensor Core acceleration for deep learning workloads 
• Hardware-aware model optimization techniques 
• Benchmarking GPU inference performance 
• Case Study: Accelerating medical imaging AI analysis using GPU optimization.

Module 4: AI Inference Engines and Runtime Optimization

• Working with TensorRT optimization pipelines 
• ONNX Runtime performance acceleration 
• OpenVINO model optimization workflows 
• Runtime graph optimization techniques 
• Building efficient inference execution environments 
• Case Study: Deploying a computer vision application with optimized inference using TensorRT.

Module 5: Large Language Model (LLM) Inference Optimization

• Understanding LLM inference challenges 
• Optimizing transformer-based AI models 
• KV cache optimization techniques 
• Efficient token generation strategies 
• Scaling generative AI inference workloads 
• Case Study: Improving enterprise AI assistant response speed using LLM optimization methods.

Module 6: Edge AI and Embedded Inference Optimization

• Designing AI solutions for edge devices 
• Lightweight model deployment strategies 
• Optimizing AI models for IoT environments 
• Energy-efficient inference techniques 
• Managing resource constraints on edge hardware 
• Case Study: Deploying a real-time object detection system on an industrial edge device.

Module 7: Cloud AI Inference Scaling and MLOps

• Building scalable AI inference services 
• Containerized AI deployment using Kubernetes 
• Model serving architectures and APIs 
• Automated monitoring and optimization pipelines 
• Managing AI infrastructure costs 
• Case Study: Scaling a recommendation engine for millions of online users.

Module 8: AI Performance Testing and Future Optimization Trends

• AI inference benchmarking methodologies 
• Profiling AI applications for bottleneck detection 
• Performance monitoring and continuous optimization 
• Emerging AI acceleration technologies 
• Future trends in efficient AI computing 
• Case Study: Optimizing a financial fraud detection AI platform for real-time transactions.

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.