Model Optimization and Compression Techniques

As deep learning models grow in size and complexity, enterprises face challenges in deploying them efficiently across various environments—from edge devices to large-scale cloud platforms. Model optimization and compression techniques such as quantization, pruning, and knowledge distillation enable AI teams to reduce latency, memory footprint, and cost without sacrificing accuracy. This 7-day enterprise training program equips participants with hands-on experience and strategic understanding of model compression techniques, providing the tools to deliver high-performance AI applications optimized for production, scalability, and sustainability.

Objectives of the Training

Understand the need for model optimization and the trade-offs between speed, accuracy, and size.
Learn various model compression techniques, including pruning, quantization, and distillation.
Gain hands-on experience optimizing neural networks using TensorFlow, PyTorch, and ONNX.
Learn performance benchmarking and deployment strategies for edge and cloud environments.
Develop enterprise-grade workflows for sustainable and efficient AI deployment.

Prerequisites

Intermediate proficiency in Python programming.
Familiarity with deep learning concepts and neural network architectures.
Prior experience using TensorFlow or PyTorch.
Understanding of matrix operations, backpropagation, and model evaluation metrics.

What You Will Learn

Model compression techniques: pruning, quantization, and knowledge distillation.
Performance tuning for inference acceleration.
Mixed precision and low-bit optimization for GPUs and TPUs.
Benchmarking and profiling optimized models.
Cloud and edge deployment best practices for optimized AI systems.
Real-world use cases across industries, including autonomous systems, mobile AI, and IoT devices.

Target Audience

This course is ideal for Machine Learning Engineers, AI Developers, and Data Scientists responsible for model deployment and optimization. It also benefits System Architects, Cloud Engineers, and AI Infrastructure Specialists aiming to reduce computational costs while maintaining high model performance.

Detailed 7-Day Curriculum

Day 1 – Introduction to Model Optimization and Performance Bottlenecks (6 Hours)

Session 1: Understanding Model Complexity and Deployment Challenges.
Session 2: Introduction to Model Optimization Techniques and Objectives.
Session 3: Overview of Quantization, Pruning, and Knowledge Distillation.
Hands-on: Profiling Deep Learning Models to Identify Bottlenecks.

Day 2 – Quantization Fundamentals and Techniques (6 Hours)

Session 1: Introduction to Quantization – Post-Training vs. Quantization-Aware Training.
Session 2: Fixed-Point Arithmetic, Dynamic Range Quantization, and INT8 Optimization.
Session 3: Frameworks and Tools – TensorFlow Lite, PyTorch FX, and ONNX Runtime.
Hands-on: Quantizing a Pretrained Model for Edge Deployment.

Day 3 – Pruning for Model Size Reduction (6 Hours)

Session 1: Structured vs. Unstructured Pruning Techniques.
Session 2: Sparsity Regularization and Weight Pruning Methods.
Session 3: Pruning Workflow using TensorFlow Model Optimization Toolkit and PyTorch Sparse API.
Case Study: Pruning CNN Models for Efficient Inference on Mobile Devices.

Day 4 – Knowledge Distillation and Model Transfer (6 Hours)

Session 1: Fundamentals of Knowledge Distillation – Teacher-Student Paradigm.
Session 2: Implementing Distillation for CNNs and Transformers.
Session 3: Combining Pruning, Quantization, and Distillation for Maximum Efficiency.
Hands-on: Building a Compact Student Model with Minimal Accuracy Loss.

Day 5 – Benchmarking, Profiling, and Optimization Workflows (6 Hours)

Session 1: Performance Measurement Metrics – Latency, Throughput, and Power Usage.
Session 2: GPU and TPU Profiling using NVIDIA Nsight and TensorBoard.
Session 3: Mixed Precision and Layer Fusion Techniques for Speed Optimization.
Workshop: Benchmarking Optimized Models on Edge Devices and Cloud GPUs.

Day 6 – Deployment Strategies and Real-World Applications (6 Hours)

Session 1: Exporting and Deploying Optimized Models using ONNX and TensorRT.
Session 2: Cloud Deployment using AWS Sagemaker, Azure ML, and GCP Vertex AI.
Session 3: Real-World Use Cases – Predictive Analytics, Autonomous Systems, and IoT Edge AI.
Hands-on: Deploying an Optimized Model Pipeline for a Production Use Case.

Day 7 – Capstone Project & Future Trends in Model Optimization (6 Hours)

Session 1: Capstone Project – End-to-End Optimization of a Deep Learning Pipeline.
Session 2: Project Presentation and Review of Performance Gains.
Session 3: The Future of Model Optimization – Neural Architecture Search, TinyML, and Efficient AI.
Panel Discussion: Enterprise AI Sustainability and Resource Optimization.

Capstone Project

Participants will design and optimize a deep learning model using the full suite of compression techniques learned during the course. Possible projects include compressing a large NLP or image classification model for edge deployment or optimizing a production-ready LLM for reduced inference latency. Each participant will benchmark pre- and post-optimization performance and present measurable improvements in model efficiency.

Future Trends in Model Optimization and Efficient AI

Model optimization is rapidly evolving with emerging technologies like Neural Architecture Search (NAS), adaptive quantization, and automated pruning frameworks. As enterprises adopt AI at scale, sustainability and compute efficiency are becoming core business objectives. Future AI systems will prioritize lightweight architectures, hybrid edge-cloud optimization, and hardware-aware model design—enabling faster, greener, and smarter AI solutions across industries.