Can I take this if I’m not an ML engineer?

<text variant="body1">Absolutely. It’s suitable for software engineers, DevOps, and data professionals transitioning into GenAI roles.

What’s the final project?

<text variant="body1">A complete GenAI deployment project, integrating LLMs, RAG, safety, and monitoring into an enterprise-ready solution.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

GenAI Data and Analytics Academy Specialization

New! Discover how 91% of learners achieved at least one positive career outcome. Learn more.

GenAI Data and Analytics Academy Specialization

GenAI Data and Analytics Academy. Master GenAI, LLMs & Agents: From Data Pipelines to Model Deployment at Scale

Instructors: Starweaver

Included with Coursera Plus

Learn more

4 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

1 month at 4 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

4 course series

Get in-depth knowledge of a subject

Intermediate level

Recommended experience

1 month at 4 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

Design and implement comprehensive data pipelines for GenAI applications.
Develop and deploy autonomous AI agents and multi-agent systems.
Analyze prompt engineering and chain management for LLMs.
Build production-ready GenAI applications with monitoring and safety systems.

Overview

Ever wondered how ChatGPT-like systems are built and deployed in the real world? Ready to move beyond basic prompts to creating your own AI agents? Welcome to "Generative AI Engineering" – your complete guide to building production-ready AI systems that actually work.

In this comprehensive journey, you'll master not just the theory but the actual engineering practices that power today's most advanced AI systems. From crafting robust data pipelines that feed your models, to building autonomous agents that can reason and act on their own, to deploying systems that scale reliably in production – we've got you covered. Whether you're looking to build the next groundbreaking AI application or enhance existing systems with state-of-the-art generative capabilities, this course provides the practical, hands-on experience you need to make it happen.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Starweaver

Specialization - 4 course series

Learners will complete practical, hands-on projects that mirror real-world GenAI challenges—such as designing prompt chains, building RAG systems, fine-tuning LLMs, and deploying AI agents with monitoring and safety protocols. These projects help learners apply engineering skills to build scalable, production-ready generative AI applications that solve authentic problems in business and technology.

GenAI Foundations and Prompt Engineering

Course 16 hoursView course

What you'll learn

Master GenAI foundational concepts and LLM integration strategies for building scalable enterprise applications.
Construct advanced prompt engineering workflows with multi-step chains and context optimization.
Implement enterprise GenAI use cases with systematic design and ROI measurement frameworks.
Develop production-ready conversation systems with template engines and quality assurance protocols.

Skills you'll gain

Prompt Engineering, Generative AI, Prompt Patterns, Performance Tuning, Program Evaluation, Enterprise Application Management, Systems Architecture, Business Metrics, Application Programming Interface (API), Application Design, LLM Application, Debugging, Large Language Modeling, Artificial Intelligence and Machine Learning (AI/ML), Development Environment, ChatGPT, Generative AI Agents, and Process Optimization

GenAI Data Engineering and RAG Systems

Course 26 hoursView course

What you'll learn

Construct enterprise-grade data processing pipelines with quality validation and AI-ready formatting.
Implement sophisticated RAG architectures with vector search, embeddings, and context management.
Deploy advanced RAG optimization techniques including reranking, metadata filtering, and adaptive strategies.
Develop specialized customer support RAG systems with context-aware personalization and performance tracking.

Skills you'll gain

Generative AI, Data Pipelines, Data Quality, Data Processing, Enterprise Architecture, Database Systems, Data Architecture, Extract, Transform, Load, Prompt Engineering, Semantic Web, Engineering, LLM Application, System Monitoring, AI Personalization, Talent Pipelining, Scalability, Process Optimization, Performance Tuning, Continuous Monitoring, and Query Languages

GenAI Foundations and AI Agents Development

Course 36 hoursView course

What you'll learn

Construct autonomous AI agents with CrewAI framework, tool integration, and decision-making capabilities.
Implement sophisticated multi-agent coordination systems with communication protocols and task delegation.
Deploy specialized customer support agents with knowledge base integration and escalation management.
Apply agent safety frameworks and testing protocols for reliable production deployment.

Skills you'll gain

Generative AI Agents, Large Language Modeling, Software Architecture, LLM Application, Generative AI, Scalability, Prompt Patterns, OpenAI, Collaboration, System Design and Implementation, Logical Reasoning, LangChain, Data Integration, Application Deployment, Artificial Intelligence, Tool Calling, Agentic systems, Job Evaluation, and Responsible AI

GenAI Model Development and Production Engineering

Course 410 hoursView course

What you'll learn

Execute advanced fine-tuning workflows including LoRA optimization and domain-specific model customization.
Implement enterprise-scale deployment strategies with containerization, monitoring, and infrastructure automation.
Construct comprehensive production monitoring and maintenance protocols with automated alerting and performance tracking.
Apply advanced optimization techniques including caching, edge deployment, hybrid routing, and emerging technology adoption strategies.

Skills you'll gain

Application Deployment, MLOps (Machine Learning Operations), Performance Tuning, Generative AI, Cloud Infrastructure, Site Reliability Engineering, Kubernetes, Scalability, Job Evaluation, Process Optimization, Docker (Software), Containerization, Technology Strategies, Automation, Enterprise Architecture, Infrastructure Architecture, Large Language Modeling, Continuous Monitoring, Prompt Engineering, and Continuous Deployment