AI Infrastructure for Engineers
A practical look at the infrastructure patterns supporting AI workloads, deployment, and production operations.
Who it’s for
- Platform engineers
- Backend engineers
- AI application teams
Preview the learning experience
Sample lesson
AI Infrastructure Foundations Preview
Draft preview
A planned preview covering platform patterns, deployment flow, and operational readiness for AI workloads.
- Review workload and model-serving patterns.
- Identify infrastructure requirements and constraints.
- Start with observability and dependable deployment practices.
Sample lab
Sample Lab Preview
A draft operational lab will be published when the course content is finalized.
- Evaluate the infrastructure requirements for a model-serving workflow.
- Review deployment patterns and operational readiness.
Instructor snapshot
Tulika Gupta
Tulika Gupta brings more than 15 years of hands-on experience in production infrastructure, DevOps, distributed systems, and cloud operations. Her work spans Kafka, Redis, event-driven systems, observability, and high-availability architectures in real production environments.
Why this matters
- 15+ years of hands-on experience building and operating production infrastructure.
- Practical experience across Kafka, Redis, messaging, cloud operations, and reliability engineering.
- Focused on real-world troubleshooting, system behavior, and production decision-making.
- Helps teams improve reliability, observability, and operational resilience without relying on theory alone.
Format
Live, instructor-led remote sessions
Prerequisites
- Experience with cloud infrastructure and production application operations
Enrollment, support, and policy
Payment & checkout
To be announced.
Support
Community and post-course support options will be announced before the cohort begins.
Enrollment policy
- Refund requests are considered on a case-by-case basis before the course start date and only when the enrollment terms have been explicitly approved by the founder.
- Students who withdraw before the course start date may be eligible for a credit or partial refund based on the approved policy and the amount of course preparation already incurred.
- If the instructor or cohort is rescheduled, students will be offered the next available cohort or a comparable alternative within the same course track when feasible.
- A cohort may be cancelled or rescheduled if minimum enrollment requirements are not met, and students will be notified in advance.
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