AI Engineer MLOps Track: Deploy Gen AI & Agentic AI at Scale

AI Engineer MLOps Track: Deploy Gen AI & Agentic AI at Scale

AI Engineer MLOps Track: Deploy Gen AI & Agentic AI at Scale is a production-focused course designed for developers and AI engineers who want to deploy, scale, and operate real-world Generative AI and Agentic AI systems across modern cloud platforms.

Rather than stopping at model building, this course focuses on end-to-end AI delivery—from SaaS-style LLM apps to enterprise-grade, observable, secure, and scalable AI systems deployed on AWS, GCP, Azure, and Vercel. It bridges the gap between GenAI development and MLOps, showing how modern AI products are actually shipped in production.

Course Snapshot

• Learners Enrolled: 23,900+

• Content Length: ~18.5 hours

• Skill Level: Intermediate (with beginner-friendly labs included)

• Language: English (Auto captions), Spanish (Auto captions)

• Certification: Certificate of completion included

• Access: Lifetime access (mobile & TV supported)

• Rating: 4.7 / 5

• Pricing: Available via Udemy subscription or discounted purchase

What This Course Actually Covers

This course is structured as a hands-on production journey, guiding learners through deploying LLM-powered SaaS applications and agentic systems across multiple cloud providers.

Instead of isolated demos, learners build and deploy:

• Full-stack AI SaaS applications

• Secure, authenticated AI systems

• Multi-agent and agentic-loop architectures

• Cloud-native, MLOps-enabled AI deployments

The emphasis is on real deployment decisions, not toy examples.

Complete MLOps Bootcamp With 10+ End To End ML Projects

Start Course

Skills & Concepts You’ll Develop

Production LLM & SaaS Deployment

• Deploy AI-powered SaaS apps to Vercel, AWS, Azure, and GCP

• Manage authentication and subscriptions using Clerk

• Handle environment configuration and API cost controls

Cloud Architecture for AI

• Design AI-ready cloud architectures using:

  • AWS Lambda, S3, CloudFront, SQS, Route 53

  • API Gateway and App Runner

• Move from serverless to containerized AI systems

GenAI Platforms & Models

• Integrate with Amazon Bedrock and SageMaker

• Build with modern models including GPT-5, Claude 4, AWS Nova, OSS models, and Hugging Face

• Deploy Retrieval-Augmented Generation (RAG) systems

MLOps & Infrastructure Automation

• Deploy across Dev, Test, and Prod environments

• Use Terraform for infrastructure as code

• Implement CI/CD pipelines with GitHub Actions

Agentic AI & Multi-Agent Systems

• Build multi-agent systems and agentic loops

• Use Amazon Bedrock AgentCore and managed agent frameworks

• Apply guardrails, monitoring, explainability, and control

Observability, Security & Enterprise Readiness

• Monitor AI systems for reliability and performance

• Apply enterprise-grade security patterns

• Build explainable, observable, and cost-controlled AI solutions

Who This Course Is Best Suited For

• AI Engineers moving into production and MLOps

• Developers deploying GenAI SaaS applications

• Engineers working with AWS, Azure, GCP, or Vercel

• Professionals building agentic and multi-agent AI systems

• Teams needing scalable, secure AI deployments

Common Questions Learners Ask

Do I need strong coding experience?
Basic Python and LLM familiarity helps, but the course includes self-study labs for foundational skills.

Is this about model training?
No. The focus is on deployment, scaling, and operations, not training models from scratch.

Does it cover real cloud deployments?
Yes. You deploy live AI applications across multiple cloud platforms.

Will this help with enterprise AI work?
Yes. Topics like observability, security, guardrails, and CI/CD are central to the course.

Practical Value

What makes this course stand out is its production-first mindset. Learners see how modern AI systems move from prototype to commercial, scalable, and monitored products—including authentication, billing, infrastructure automation, and agent orchestration.

It’s especially valuable for anyone aiming to work as an AI Engineer, MLOps Engineer, or GenAI Platform Engineer.

Final Thoughts

If you already understand Generative AI basics and want to learn how AI systems are actually deployed and operated at scale, this course offers a clear, hands-on path. It focuses on real infrastructure, real cloud constraints, and real business use cases, making it a strong bridge between AI development and production engineering.