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AI SYSTEMS ENGINEERING & RELIABILITY COURSE

Learn the skills to deploy, monitor, scale, and maintain production-grade AI systems in real-world cloud environments. Our AI Systems Engineering & Reliability Course gives you the skills, hands-on practice, and creative and technical confidence to bridge the gap between building AI models and keeping them running reliably while maintaining performance under pressure, implementing proactive monitoring, and responding to incidents with precision and data-informed decision-making. transformation.  

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Learn in-demand skills to thrive in the AI era. Tell us a little about you and we’ll get in touch with more info.

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Residents of Alabama, Connecticut, Kentucky, Nebraska, New York, Oklahoma, District of Columbia, Wisconsin, and Wyoming are not eligible to enroll in this program.

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Want to learn more? Get answers to common questions and discover what makes learning with GA different. 

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OUR LEARNERS WORK AT TOP COMPANIES ACROSS THE GLOBE

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PURCHASING MADE EASY

The total cost of this course is $2,950.

Discounts available for those who qualify.

Divide tuition into two, three, or four easy payments while in school.

As low as $712.50.

Apply for a 0% interest loan from Climb Credit

Pay zero interest on manageable payments over 9 months with the 0% Interest Loan.

Loan approval subject to eligibility.

Apply for a loan from Climb Credit.

Begin repaying immediately, or choose an interest-only option.

Get an interest rate from 6.5–15%, with a Climb loan term from 2–5 years or an interest rate ranging 6.99 – 17.99% APR.

Loan approval subject to eligibility. Loan terms displayed are effective as of 1/1/2026.

Show off your new skills

Complete your course, get your badge, and add it to your LinkedIn profile to showcase your new skills to your network.
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Who’s this for?

This course is for DevOps and infrastructure engineers, ML engineers, data scientists, site reliability engineers, operations teams, technical managers, and platform leaders seeking to extend their skills into AI-specific operational challenges and who want:

  • Hands-on experience with AI tools applied to real AI systems engineering scenarios
  • Learning with structure, community, and live instruction
  • Skills you can apply immediately to current projects

TECHNICAL SETUP

 

  • Laptop with administrator access
  • 13"+ screen, 8GB RAM, and 40GB free storage
  • Stable internet with dual-monitor setup and webcam for online sessions
  • Full technical setup guide and support provided after enrollment

RECOMMENDED EXPERIENCE

This course is designed to be accessible while building toward advanced operational practices. Learners will benefit from:

  • Basic understanding of how AI applications are built and deployed
  • Familiarity with programming concepts, cloud environments, or prior experience in data or software workflows

No prior experience with reliability engineering or DevOps is required.

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BRING YOUR OWN AI 

This course works with any major AI platform. Use your existing account or free tiers from: OpenAI ChatGPT, Google Gemini, Anthropic Claude, Microsoft Copilot, or Perplexity. No premium subscriptions required—learn with the tools you already have.

Course Agenda

  • Build a foundational understanding of AI system operations, cloud environments, and infrastructure automation
  • Learn how data, models, and services interact in production systems, deploy environments using Infrastructure as Code with Terraform, and apply DevOps principles to maintain consistency and performance across AI workloads
  • Apply reliability engineering principles, implement observability and monitoring tools, and learn structured approaches to incident response and recovery
  • Explore SLIs, SLOs, and error budgets, configure monitoring dashboards with Prometheus and Grafana, and practice postmortem analysis to strengthen fault tolerance
  • Implement continuous integration pipelines, containerization, and deployment strategies that enable scalability and rapid iteration
  • Gain hands-on experience automating workflows, deploying with Kubernetes and ArgoCD, and designing systems that stay performant and secure at scale
  • Design scalable architectures, apply DevSecOps principles to protect models and data, and tune system performance for efficiency at scale
  • Learn horizontal and vertical scaling strategies, implement security and governance best practices, and optimize cost-to-performance ratios
  • Apply all operational and reliability skills to optimize, audit, and validate AI systems in production
  • Conduct reliability audits, implement continuous improvement strategies, and complete a capstone project demonstrating end-to-end operational excellence

WHAT MAKES THIS PROGRAM DIFFERENT 

✓ Live cohort learning: Learn with a structured group of professionals who share your goals. Get real-time answers from expert instructors during live sessions.

✓ Comprehensive skill stack: Master the complete AI operations lifecycle—from infrastructure automation and reliability engineering to incident response, scaling, and continuous improvement—developing the skills and confidence to keep AI-enabled systems stable, secure, and efficient after deployment.

✓ Hands-on practice: Learn through 17 hands-on lab hours with projects designed to help you build the operational expertise to deploy, monitor, and maintain AI systems that actually work in production.

✓ Workplace-relevant application: Learn to implement observability, automation, and resilience engineering practices that define production-grade AI operations—focusing on practical skills, industry-standard tools, and the operational excellence that most AI initiatives lack.

AI IS CHANGING WORK—WE HELP YOU STAY AHEAD 

  • 85%

    85% of enterprises have adopted AI initiatives, but only 53% report confidence in their ability to monitor and govern these systems

    (Source: cloudfactory)

  • 80%

    More than 80% of AI projects fail—twice the rate of non-AI IT projects

    (Source: Rand)

  • 39%

    Only 39% of organizations are building reliable internal frameworks to support AI adoption

    (Source: ITPro)

KEY TAKEAWAYS  

You'll leave this course with these AI systems engineering skills:

DEPLOY AND MANAGE AI SYSTEMS IN CLOUD ENVIRONMENTS

You'll develop expertise in operating AI-enabled systems across distributed, cloud-based environments including AWS, GCP, and Azure. Learn to provision infrastructure using Terraform and Infrastructure as Code, manage containerized deployments with Docker and Kubernetes, and build CI/CD pipelines that support continuous integration and model updates.

IMPLEMENT OBSERVABILITY, MONITORING, AND INCIDENT RESPONSE

You'll gain practical experience building observability stacks and alerting systems to track performance, detect drift, and prevent downtime. Master Prometheus and Grafana for real-time monitoring, apply SLIs, SLOs, and error budgets to measure reliability, and practice structured incident response with root cause analysis and postmortem documentation.

SCALE, SECURE, AND CONTINUOUSLY IMPROVE AI OPERATIONS

You'll learn to design scalable architectures with redundancy, failover, and automated recovery while applying DevSecOps principles to protect models and data. Develop skills in performance testing, cost optimization, reliability audits, and chaos engineering—culminating in a capstone project that demonstrates production-ready operational excellence.

INSTRUCTORS WITH REAL-WORLD CRED

Learn from real-world AI systems engineering pros who bring hands-on experience straight from the field to the classroom. Every GA instructor is committed to giving the personalized feedback and support you need to crush your goals every step of the way.
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THE WORD FROM GA GRADS

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“Getting exposure and time with our instructor and classmates meant we could get to know other industries and how they approach marketing problems. This course gave me the confidence in my decision to move to marketing.”
Kiki Tolentino

GA grad, Digital Marketing Short Course

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GET MORE INFO

Learning AI skills is no longer optional. Tell us a little about you—and we’ll get in touch with more info.

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Let’s Chat

Need to speak with someone directly?
Our admissions team is here to help.

North America
+1 844 969 4669
UK
+44 20 3991 6088
Singapore
+65 6018 7933
Australia
+61 1800 845 068

QUESTIONS? WE'VE GOT ANSWERS.

AI is reshaping every role and every industry, and learning AI skills to enhance your role is no longer optional. General Assembly's AI Systems Engineering & Reliability is live, cohort-based training that equips learners with the practical skills to deploy, monitor, scale, and maintain production-grade AI systems in real-world cloud environments. In 32 hours, you’ll master the complete AI operations lifecycle—from infrastructure automation and reliability engineering to incident response, security, and continuous improvement.
Yes. When you pass this course, you’ll receive a LinkedIn-verified digital badge. Thousands of GA alumni around the world use their course badge to demonstrate their skills to their LinkedIn networks, potential employers, and more. Our courses are well-regarded by many top employers, who contribute to our curriculum and partner with us to train their own teams.

Our AI Systems Engineering & Reliability Course is designed for DevOps and infrastructure engineers, ML engineers, data scientists, site reliability engineers, operations teams, technical managers, and platform leaders seeking to extend their skills into AI-specific operational challenges. Student backgrounds include:

  • DevOps professionals wanting to understand ML system requirements and patterns
  • Data scientists wanting to understand what happens after model handoff
  • Engineers seeking to own the full lifecycle from development to production
  • Leaders seeking to reduce AI project failure rates in their organizations

This course is designed to be accessible while building toward advanced operational practices. No prior experience with reliability engineering or DevOps is required. Learners will benefit from a basic understanding of how AI applications are built and deployed—familiarity with programming concepts, cloud environments, or prior experience in data or software workflows will help them get the most from the hands-on labs.

Our Admissions team can discuss your background and learning goals to advise if this course is a good fit for you.

General Assembly's AI Systems Engineering & Reliability is live, cohort-based training that equips learners with the operational expertise to deploy, monitor, scale, and maintain production-grade AI systems in real-world cloud environments. The course teaches industry-standard tools like Terraform, Docker, Kubernetes, Prometheus, Grafana, and ArgoCD to help learners master the complete AI operations lifecycle—from infrastructure automation and reliability engineering to incident response, security, and continuous improvement. In 32 hours, you’ll learn to bridge the gap between building AI models and keeping them running reliably, all while maintaining performance under pressure, implementing proactive monitoring, and responding to incidents with precision and data-informed decision-making. You’ll also receive a LinkedIn-verified digital badge upon successful course completion.

Key learning outcomes include:

  • Deploying and managing AI systems in cloud environments
  • Implementing observability, monitoring, and incident response
  • Scaling, securing, and continuously improving AI operations
Yes. All of our courses are designed for busy professionals with full-time work commitments. There’s no prework or homework, and the workload is designed to be manageable with a full-time job. If you need to miss a session or two, we offer resources to help you catch up. We recommend you discuss any planned absences with your instructor.

Our Admissions team is here to help and can advise whether this course is right for you and your learning goals. You can also:

*Course modality options vary by location, pending market availability. Please contact our Admissions team to discuss what version is available in your location.

Education does not guarantee outcomes, including, but not limited to, employment or future earnings potential.
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