Gain advanced generative AI skills

Taking generative AI from experimentation to production requires more than effective prompting. On this three-week course, you’ll learn how to specialise models, build multimodal AI applications, and apply the engineering practices needed to operate generative AI systems reliably.

You’ll explore when fine-tuning is more appropriate than prompting or retrieval, then use parameter-efficient techniques including LoRA, QLoRA, and adapters through the Hugging Face ecosystem. You’ll prepare instruction-tuning datasets and evaluate model performance before and after fine-tuning.

Build and deploy multimodal AI applications

Extend your generative AI capabilities beyond text. You’ll work with vision-language models for image captioning and visual question answering, as well as speech-to-text and text-to-speech technologies.

You’ll then learn how to deploy generative AI applications using FastAPI, containerisation, and cloud platforms. By combining these technologies, you’ll develop the skills to turn AI models into usable applications that can operate beyond a development environment.

Learn to manage AI systems in production

Discover the practices that help organisations operate LLM applications effectively throughout their lifecycle. You’ll explore LLMOps, including prompt and output versioning, experiment tracking, tracing, observability, and model drift monitoring.

You’ll also learn how to evaluate and improve AI systems. The course culminates in a project where you’ll build, deploy, evaluate, and monitor a production-ready generative AI application.

By the end, you’ll have practical experience that will help you progress towards roles such as Generative AI Engineer, LLM Engineer, and Applied AI Engineer.

Syllabus

Week 1
Fine-Tuning and Model Specialisation
- When and How to Fine-Tune
  In this Edureka course, learn when fine-tuning is appropriate and how to apply it to adapt AI models for specific tasks and use cases.
- Parameter-Efficient Fine-Tuning
  In this Edureka course, learn how parameter-efficient fine-tuning methods adapt AI models with fewer trainable parameters, improving efficiency and resource usage.
Week 2
Multimodal Systems and Deployment
- Building Multimodal Applications
  In this Edureka course, learn how to build multimodal applications that combine text, images, and other data types to create more capable Generative AI solutions.
- Deploying GenAI Applications
  In this Edureka course, learn how to deploy Generative AI applications and prepare them for reliable, scalable, and practical use in real-world environments.
Week 3
LLMOps, Safety, and the Capstone
- LLMOps and Production Safety
  In this Edureka course, learn how LLMOps practices and production safety measures help build, monitor, and maintain reliable and secure Generative AI applications.
- Observability, Monitoring, and the Capstone
  In this Edureka course, learn how observability and monitoring help track GenAI application performance, reliability, and behaviour, then apply these practices in a practical capstone project.

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Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

Describe the roles of fine-tuning, prompting, and RAG in generative AI model specialisation.
Experiment LoRA, QLoRA, and adapter-based fine-tuning using the Hugging Face ecosystem.
Assess GenAI systems using model evaluation, guardrails, red-teaming, tracing, and drift monitoring.
Design an end-to-end production GenAI solution incorporating specialisation, deployment, safety, observability, and LLMOps practices.
Compare multimodal and deployment approaches involving vision, speech, FastAPI, Docker, and cloud platforms.

Who is the course for?

This course focuses on specialising models, building multimodal systems, and running GenAI reliably in production. It is for learners who can build LLM applications and RAG systems and who now want to adapt models to specific tasks and operate them dependably.

It is ideal for aspiring GenAI engineers and LLM engineers, and developers preparing for roles where AI systems must be fine-tuned, deployed, monitored, and maintained. Learners should be confident with LLM APIs, frameworks, and RAG before starting. By the end, they will have built, deployed, and monitored an end-to-end GenAI application and will be ready for generative AI engineer roles.

Who will you learn with?

Edureka Instructors

Who developed the course?

Edureka

Edureka is a global online learning platform offering industry-focused training in emerging technologies such as Cloud, AI, GenAI, Data Science, DevOps, and Cybersecurity, helping professionals upskill and advance their careers.

About this ExpertTrack

Build job-ready generative AI and LLM expertise, from prompt engineering and application development to RAG and AI systems.

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