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MLOPS & AI INFRASTRUCTURE COURSE

Learn to design, deploy, and govern enterprise-grade AI systems at scale. Our MLOps & AI Infrastructure course gives you the skills, hands-on practice, and creative and technical confidence to move beyond model building and focus on the complete AI lifecycle—from pipeline architecture and data infrastructure management to governance, security, and strategic AI operation leadership.

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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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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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Build your AI and machine learning foundation

This course is one of four courses in the AI & Machine Learning pathway, designed to help learners understand, build, and apply machine learning models in real-world contexts.

You can take this course on its own, or complete the full pathway to deepen your understanding of machine learning concepts, model development, and applied AI techniques.

More courses in this pathway:

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the Total cost of this course is $2,950

Take two courses and qualify for an additional two courses for free. With a bundle discount, all four courses are available for a total tuition of $5,900.*

*Eligibility is based on terms and conditions.

When you enroll in two eligible courses, you become eligible for a bundle discount that allows you to take the remaining two courses at no additional tuition and fee costs.

The bundle discount applies only after enrollment in two qualifying courses. Students must enroll in the four courses individually and are charged applicable tuition and fees after enrollment occurs.

Bundle eligibility, course availability, and timing are subject to terms and conditions.

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

As low as $712.50.

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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 ML engineers, data scientists, technical managers, AI platform leaders, DevOps and infrastructure professionals, software engineers, and backend developers looking to expand their AI/ML expertise and who want:

  • Hands-on experience with AI tools applied to real MLOps 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

  • Foundational understanding of data analytics, cloud computing concepts, or basic programming logic
  • Familiarity using collaborative tools such as GitHub or command-line interfaces
  • Prior experience with Python, SQL, or any cloud service (e.g., Google Cloud or AWS) is beneficial
  • Curiosity about how artificial intelligence moves from experimentation to production and willingness to explore enterprise-scale infrastructure challenges

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BRING YOUR OWN AI 

Take this course using any major AI tool. No premium subscriptions required.


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Course Agenda

  • Investigate how to design and operationalize production-grade AI systems that are scalable, reliable, and automated
  • Examine how data, models, and orchestration layers interact across the AI lifecycle, applying engineering principles that support large-scale, sustainable deployments
  • Design and manage the data infrastructure required to support scalable AI systems
  • Examine how data architecture, lineage, quality, and governance contribute to model performance and compliance within production environments
  • Operationalize AI models through containerization, deployment, and monitoring practices that ensure reliable, continuous performance
  • Investigate how CI/CD pipelines, automated retraining, and performance monitoring sustain model accuracy and adaptability in production
  • Evaluate how governance frameworks, security policies, and optimization strategies support ethical and efficient AI operations
  • Investigate how to mitigate risks, enforce compliance, and optimize system performance while maintaining transparency and accountability
  • Integrate technical, governance, and strategic dimensions of AI infrastructure leadership
  • Synthesize architectural design, data management, and MLOps practices into a cohesive roadmap that aligns with organizational goals and ethical principles
  • Complete a capstone project producing a comprehensive AI infrastructure strategy

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 essential skills of production-grade pipeline architecture, model deployment and monitoring, and AI governance—developing hands-on capabilities with industry-standard tools to design, secure, and lead AI systems that scale.

✓ Hands-on practice: Learn through 17 hands-on lab hours with projects designed to help you build expertise in enterprise AI infrastructure with end-to-end MLOps mastery and strategic leadership capabilities.

✓ Workplace-relevant application: Learn the technical foundations of scalable pipeline design, model operationalization, and infrastructure optimization alongside the governance and strategic thinking that define modern AI leadership.

AI IS CHANGING WORK—WE HELP YOU STAY AHEAD 

  • $2.2b

    The global MLOps market is projected to grow from $2.2 billion in 2024 to $16.6 billion by 2030

    (Source: Grand View Research, 2025)

  • $23.5b

    The AI infrastructure market is estimated to grow from $23.5 billion in 2021 to $309.4 billion by 2031

    (Source: Allied Market Research, 2023)

  • 15%

    Software developer employment is projected to grow 15% the next decade

    (Source: U.S. Bureau of Labor Statistics, 2025)

KEY TAKEAWAYS  

You'll leave this course with these MLOps and AI infrastructure skills:

DESIGN AND OPERATIONALIZE PRODUCTION-GRADE AI SYSTEMS

You'll develop expertise in architecting scalable, automated AI pipelines that connect data ingestion, model training, deployment, and monitoring in production environments. Learn to design modular, reusable systems using containerization and orchestration tools like Docker and GCP Cloud Composer that support continuous integration, model retraining, and enterprise-scale reliability.

MANAGE DATA INFRASTRUCTURE AND DEPLOY MODELS WITH MLOPS PRACTICES

You'll gain practical experience designing data architectures for high-volume AI workloads while implementing governance, versioning, and compliance frameworks. Deploy and monitor machine learning models using CI/CD pipelines, establishing systems for drift detection, automated retraining, and performance optimization that maintain model accuracy in production.

LEAD AI INFRASTRUCTURE STRATEGY, GOVERNANCE, AND OPTIMIZATION

You'll develop the strategic thinking to align AI infrastructure with organizational objectives and ethical principles. Learn to implement governance and compliance frameworks, mitigate security risks, optimize resource utilization, and lead cross-functional collaboration—culminating in a comprehensive AI infrastructure roadmap that demonstrates mastery in building, securing, and leading enterprise-grade AI systems.

INSTRUCTORS WITH REAL-WORLD CRED

Learn from real-world MLOps and AI infrastructure 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 MLOps & AI Infrastructure is live, cohort-based training that equips ML engineers, data scientists, technical managers, AI platform leaders, DevOps and infrastructure professionals, software engineers, and backend developers with the end-to-end skills to design, deploy, and govern enterprise-grade AI systems at scale. In 32 hours, you’ll develop expertise in everything from pipeline architecture and data infrastructure management to governance, security, and strategic AI operation leadership.
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.

This is an advanced-level course and learners should enter the course with a solid understanding of data analytics, cloud computing concepts, or basic programming logic, along with familiarity using collaborative tools such as GitHub or command-line interfaces in order to be successful. Students should have at least 2 years of prior experience with Python, SQL, or any cloud service (e.g., Google Cloud or AWS) or have prior educational practice with these concepts to be most successful.

Admissions requirements and recommended experience are explained in additional detail in the Student Catalog. Our Admissions team can discuss your background and learning goals to advise if this course is a good fit for you.

General Assembly's MLOps & AI Infrastructure is live, cohort-based training that equips learners with the practical skills to build and manage production-ready machine learning infrastructure that bridges data science, engineering, and operations. The course moves beyond model building to focus on the complete AI lifecycle—from pipeline architecture and data infrastructure management to governance, security, and strategic leadership of AI operations within complex organizations. In 32 hours, you’ll learn industry-standard tools like Docker, GCP Cloud Composer, CI/CD pipelines, and cloud platforms to design, deploy, and govern enterprise-grade AI systems at scale. You’ll also receive a LinkedIn-verified digital badge upon successful course completion.

Key learning outcomes include:

  • Designing and operationalizing production-grade AI systems
  • Managing data infrastructure and deploying models with MLOps practices
  • Leading AI infrastructure strategy, governance, and optimization
Yes. All of our courses are designed for busy professionals with full-time work commitments. There’s no prework, 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 and eligibility. Please contact our Admissions team to discuss course eligibility and 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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