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Keep shoppingAI Model Engineering: From Concept to Deployment
Role Play
2,030 students
AI Model Engineering: From Concept to Deployment
Understand what is an AI model, how AI models are created, and implement real projects with AI ML techniques.
Created byStarweaver Experts, Ritesh Vajariya, Paul Siegel
Last updated 4/2026
English
What you'll learn
- Execute fine-tuning workflows in Gen AI Model Engineering to build custom AI models using evaluation frameworks and modern AI & machine learning techniques.
- Implement AI Model Engineering deployment strategies with scalable infrastructure, and monitoring systems.
- Discover AI engineering best practices for deploying AI models with minimal engineering overhead in modern cloud-based AI systems.
- Understand the AI engineering lifecycle, from data preparation to deployment, monitoring, and continuous model improvement.
- Build AI engineering maintenance pipelines with automated scheduling, and performance tracking for reliable AI models.
- Apply advanced GenAI deployment patterns to develop scalable applications using modern AI engineering workflows.
Explore related topics
Course content
14 sections • 69 lectures • 4h 39m total length
Requirements
- Advanced Python programming and hands-on experience with ML frameworks used in AI/ML engineering and AI model development.
- Experience with cloud platforms, containers, and deployment pipelines for scalable AI engineering workflows.
- Strong understanding of AI model training, evaluation frameworks, and performance optimization.
- Knowledge of production system architecture and best practices for deploying AI models in real-world applications.
Description
Are you frustrated with generic AI models that fail to understand your domain-specific or business requirements? You’re not alone. Many organisations struggle to move beyond demo-level AI prototypes to production-grade generative AI systems that deliver consistent and measurable business value.
This course is designed to bridge that gap, transforming you into a GenAI production engineer capable of building, scaling, and maintaining enterprise-ready generative AI applications.
Throughout the course, you will gain hands-on experience fine-tuning foundation models for domain-specific tasks, implementing scalable AI deployment architectures, and integrating safety, monitoring, and performance frameworks into real production pipelines.
You will also explore advanced techniques such as parameter-efficient fine-tuning (PEFT), retrieval-augmented generation (RAG), and robust model evaluation strategies. Additionally, you will learn to design and manage infrastructure that supports continuous learning, automated retraining, model monitoring, and high-availability AI systems for enterprise workloads.
By the end of this course, you will be able to manage the complete generative AI lifecycle from custom model development to secure deployment, scalability, and long-term maintenance.
This course goes beyond building simple chatbot demos. It focuses on creating reliable, secure, and high-performance GenAI systems that drive real business outcomes.
Join the next generation of AI engineers building mission-critical generative AI solutions for modern enterprises and become the production-ready GenAI specialist every organisation needs.
Who this course is for:
- ML Engineers specializing in production AI systems who want to strengthen their expertise in AI Model Engineering and scalable AI ML engineering workflows.
- DevOps Engineers responsible for AI engineering deployments, model monitoring, and implementing best practices for deploying AI models in production environments.
- Platform Engineers building AI infrastructure and AI engineering platforms to support Gen AI Model Engineering and large-scale AI model applications.
- Technical Architects designing scalable AI systems, defining the AI engineering roadmap, and selecting the right AI models and foundation model architectures.