Training catalog
Every course is delivered in real time by a senior practitioner, never as a pre-recorded video. Browse the catalog below, filter by track, and click a course to see its full description, objectives, outcomes, and syllabus.
In the age of AI, systems design skills matter more than ever. This course takes you from the fundamentals to advanced practice so you can design modern systems that are reliable and scalable — capable of solving any problem you encounter.
Throughout the program you'll learn to reason about the essential components of distributed architecture: databases, caches, load balancers, queues, and microservices — and how to combine them into robust solutions. You'll understand the tradeoffs behind every decision and learn to communicate them clearly.
You'll study and understand the architecture of massive-scale systems like YouTube, WhatsApp, and Uber, breaking them down to discover how they handle millions of users, high availability, and inevitable failures. By the end, you'll have the technical judgment and communication skills needed to lead design discussions and stand out in professional forums.
Modern engineering teams are expected to design, provision, and operate infrastructure that is elastic, resilient, and cost-efficient — often across multiple layers of abstraction simultaneously. This course gives engineers a practical, end-to-end command of the cloud stack: from foundational AWS services to infrastructure-as-code discipline with Terraform, to running production workloads on Kubernetes.
Through guided labs and real-world scenarios, you will learn to provision and secure AWS compute, storage, and networking resources, then codify that infrastructure with Terraform to make it repeatable, auditable, and safe to change. You will move beyond "it works" configurations to understand state management, module design, and team workflows that scale across environments and organizations.
The course culminates in deploying and operating containerized applications on Kubernetes, wiring in CI/CD, observability, and cost controls so that what you build is not just functional but production-grade. By the end, you will have the judgment to make sound infrastructure decisions and the technical depth to implement, secure, and troubleshoot them independently.
The Certified Kubernetes Administrator (CKA) exam, administered by the CNCF, is the industry benchmark for validating hands-on Kubernetes administration skills under real exam conditions — a live, terminal-based, time-boxed performance test rather than a multiple-choice quiz. This course prepares you to pass it with confidence by building genuine operational fluency, not just familiarity with commands.
You will work through every domain of the official CKA exam curriculum — cluster architecture and installation, workloads and scheduling, services and networking, storage, and troubleshooting — with hands-on labs that mirror the exam's task-based format. Emphasis is placed on speed and precision with kubectl, imperative commands, and kubeadm-based cluster operations, since the exam rewards efficient, correct execution over theoretical knowledge alone.
Beyond technical mastery, the course trains exam-taking strategy: time allocation across weighted domains, effective use of official documentation during the exam, and full-length timed practice exams that simulate real conditions. Graduates leave not only exam-ready but equipped with the diagnostic instincts of a working Kubernetes administrator.
In an era where large language models underpin everything from developer tooling to customer-facing products, understanding how they actually work is no longer optional for technical teams — it's foundational literacy. This course demystifies the transformer architecture, the training and inference pipeline, and the practical techniques that separate effective LLM usage from guesswork. You'll leave with a working mental model of what these systems can and cannot do, and why.
Rather than treating LLMs as a black box API, we build intuition from the ground up: how text becomes tokens, how tokens become vectors, and how attention lets models reason over context. We then move quickly into applied territory — prompting strategies, the tradeoffs between fine-tuning, retrieval-augmented generation, and prompting, and how to evaluate model outputs rigorously rather than anecdotally.
In the age of AI, systems design skills matter more than ever — and agentic systems represent the newest, fastest-moving frontier of that discipline. This course takes you from first principles to advanced practice so you can design agent systems that are reliable, observable, and scalable enough to handle real production workloads, not just demos. You'll learn to reason about the components that make agents work: planning loops, tool use, memory, and orchestration across multiple agents.
Throughout the program, you'll examine the tradeoffs behind every architectural decision — when to add a planning layer versus a simple ReAct loop, how much autonomy to grant an agent, and how to keep costs and latency under control as complexity grows. You'll finish able to design agentic systems with the same rigor and clarity you'd bring to any distributed system, and to communicate those design decisions convincingly to engineering and business stakeholders alike.
Technical interviews reward a specific, learnable skill set — pattern recognition, structured problem-solving, and clear communication under time pressure — and this course is built to develop exactly that. Over an intensive, comprehensive program, you'll master the core data structures and algorithms that appear across virtually every coding interview, build fluency with the recurring patterns that unlock hard problems quickly, and develop the communication habits that separate strong candidates from technically capable ones.
Beyond algorithmic problem-solving, the course covers system design interviews and behavioral interviews as first-class components, since modern loops evaluate all three. Extensive mock interview practice, with structured feedback, is built into the program so that by the end, candidates aren't just solving problems correctly — they're performing well under realistic interview conditions, at the seniority level they're targeting.
Engineering manager interviews test a fundamentally different skill set than IC interviews — judgment under ambiguity, organizational thinking, and the ability to make and defend people decisions. This course prepares candidates for that reality, covering the leadership scenarios, case studies, and communication patterns that consistently appear in EM interview loops at top technical organizations.
Rather than generic interview coaching, the course grounds every topic in realistic case studies: designing a team's structure, running a hiring loop, handling a conflict between senior engineers, or communicating a roadmap slip to stakeholders. You'll practice structuring answers that demonstrate both technical credibility and people leadership, so you can navigate the full breadth of what EM interviews actually assess.
Fair, consistent performance management is one of the highest-leverage skills an engineering leader can develop — and one of the least formally taught. This course provides a rigorous, practical framework for setting goals, evaluating engineers fairly, and having the hard conversations that performance management inevitably requires, replacing ad hoc judgment with defensible, repeatable process.
The program covers the full performance management lifecycle: setting meaningful goals and OKRs, running calibration sessions that reduce bias, delivering feedback that actually changes behavior, and handling underperformance and promotions with equal rigor. Special attention is given to measuring engineering productivity responsibly — using frameworks like DORA metrics as a complement to, never a substitute for, thoughtful qualitative judgment.
Custom programs
We regularly design custom instructor-led programs for teams with specific goals — tell us what you're solving for.