AI Engineering

Description

This AI Engineering training course bridges machine learning theory with the engineering discipline needed to ship real-world AI applications. You will learn how to design, build, deploy, and scale production-grade AI systems using foundation models, APIs, MLOps tooling, and monitoring frameworks. The course covers everything from working with large language models to deploying with Docker and Kubernetes, security, performance, and a capstone project where you build and deploy a complete AI application.

Course Content

Module 1: AI Engineering Fundamentals

The AI engineer role vs. data scientist vs. ML researcher
The AI development lifecycle
Engineering principles: scalability, reliability, maintainability

Module 2: Working with Foundation Models

Understanding large language models and their capabilities
API-based AI development with OpenAI, Anthropic, Google
Open-source models: Llama, Mistral, Gemma
Choosing the right model for the job

Module 3: Building AI Applications

System architecture for AI-powered apps
Integrating AI APIs into web and mobile applications
Prompt engineering and prompt management
Handling streaming responses and async workflows

Module 4: Model Fine-Tuning and Customization

When to fine-tune vs. prompt engineer vs. use RAG
Fine-tuning workflows and best practices
Parameter-efficient methods: LoRA, QLoRA
Evaluation and benchmarking

Module 5: MLOps and Deployment

Containerization with Docker and Kubernetes
Model serving: REST APIs, gRPC, batch inference
CI/CD pipelines for AI projects
Tools: MLflow, Weights & Biases, Kubeflow

Module 6: Monitoring and Observability

Model drift and performance degradation
Logging, tracing, and metrics for AI systems
A/B testing AI features in production
Cost monitoring and optimization

Module 7: Security and Safety

Prompt injection and adversarial attacks
Data privacy in AI pipelines
Content moderation and safety filters
Red-teaming AI systems

Module 8: Scaling and Capstone Project

Caching strategies for AI responses
Load balancing and horizontal scaling
Build and deploy a complete production AI application
Documentation, monitoring, and handoff

Duration: 8 – 12 weeks

ENROLL NOW