2. What skills will I develop through AI inference engineering training?

You can develop skills in model serving, inference APIs, latency and throughput optimization, memory management, LLM serving, GPU utilization, model compression, monitoring, and scalable deployment. The course also covers infrastructure selection, security, reliability, and inference cost management.

3. What is AI inference engineering?

AI inference engineering focuses on efficiently running trained models so they can generate predictions or responses for users and applications. It involves areas such as model serving, performance optimization, hardware utilization, scalability, monitoring, and production reliability.

4. Does the course cover LLM inference optimization?

Yes. The course covers LLM token generation, decoding, context windows, KV caching, continuous batching, and LLM serving optimization. Learners also explore techniques for improving resource utilization and inference performance.

5. What projects are included in the AI inference engineering course?

Projects include building a model inference API, optimizing an AI model, deploying an LLM inference service, implementing inference monitoring, benchmarking performance, and designing a production inference system. These projects bring together model serving, optimization, infrastructure, and monitoring concepts.

AI Inference Engineering Course

Category Data Analytics & AI

Level Experts

Students 14 Already Enrolled

Review

(4.8)

AI inference engineering focuses on running trained artificial intelligence models efficiently, reliably, and at scale. This course explores the systems, architectures, and optimization techniques used to serve machine learning and large language models, from inference APIs and resource management to production deployment.

Learners explore batch and online inference, latency, throughput, memory management, request scheduling, caching, and model serving architectures. The curriculum also covers LLM token generation, context windows, KV caching, continuous batching, quantization, pruning, knowledge distillation, GPU optimization, multi-GPU systems, and distributed inference.

Production engineering receives dedicated attention through scalability, autoscaling, monitoring, reliability, API security, and cost management. Through structured projects, learners build inference APIs, optimize models, deploy LLM services, benchmark performance, implement monitoring, and design production-ready inference systems.

AI inference foundations, workflows, systems, and performance requirements
Model serving architectures, APIs, batch processing, and online inference
Latency, throughput, memory, batching, caching, and resource optimization
LLM inference, token generation, KV caching, and continuous batching
Quantization, pruning, distillation, compression, and hardware optimization
GPU infrastructure, scalable deployment, monitoring, security, and cost control

A basic understanding of artificial intelligence, machine learning, or software development is recommended. Familiarity with Python, APIs, cloud infrastructure, GPUs, or machine learning models can be beneficial for the more technical sections.

This course is designed for AI engineers, machine learning engineers, software developers, ML infrastructure professionals, cloud engineers, and technical AI teams. It is also suitable for professionals who want to specialize in model serving, LLM inference, performance optimization, and production AI infrastructure.

1.1 Understanding AI Inference
1.2 Training Versus Inference
1.3 AI Inference Workflows
1.4 Inference Systems and Components
1.5 Inference Performance Requirements
1.6 Common Inference Challenges

2.1 Preparing Models for Inference
2.2 Model Serving Architectures
2.3 Batch and Online Inference
2.4 Building Inference APIs
2.5 Managing Model Inputs and Outputs
2.6 Testing Inference Services

3.1 Measuring Inference Latency
3.2 Optimizing Throughput
3.3 Managing Inference Memory
3.4 Batching and Request Scheduling
3.5 Caching Inference Results
3.6 Optimizing Resource Utilization

4.1 Understanding LLM Inference
4.2 Token Generation and Decoding
4.3 Managing Context Windows
4.4 KV Cache Fundamentals
4.5 Continuous Batching Techniques
4.6 Optimizing LLM Serving

5.1 Quantization Fundamentals
5.2 Model Pruning Techniques
5.3 Knowledge Distillation
5.4 Model Compression Strategies
5.5 Hardware-Aware Optimization
5.6 Evaluating Optimization Tradeoffs

6.1 Understanding GPU Inference
6.2 GPU Memory Management
6.3 CPU and GPU Workloads
6.4 Multi-GPU Inference
6.5 Distributed Inference Systems
6.6 Selecting Inference Infrastructure

7.1 Designing Scalable Inference Services
7.2 Autoscaling AI Workloads
7.3 Monitoring Inference Performance
7.4 Managing Reliability and Failures
7.5 Securing Inference APIs
7.6 Controlling Inference Costs

8.1 Building a Model Inference API
8.2 Optimizing an AI Model
8.3 Deploying an LLM Inference Service
8.4 Implementing Inference Monitoring
8.5 Benchmarking Inference Performance
8.6 Designing a Production Inference System

An AI inference engineering course is suitable for AI engineers, ML engineers, software developers, infrastructure specialists, cloud professionals, and technical teams deploying AI models. A foundation in machine learning or software development can help learners engage with the technical material.

AI Inference Engineering Course

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