Simple, Transparent Pricing

This is a one-time payment of $249 for lifetime access. No recurring charges, no hidden fees. You pay once and have permanent access to all course materials and future updates. Students with valid .edu emails pay only $149 with the automatic student discount.

Students get an automatic $100 discount when signing up with a valid .edu email address. After registration, you'll receive the coupon code STUDENT2026 which reduces the price from $249 to $149. This offer is available to all current students with verified educational email addresses.

This course is designed for ML engineers, platform engineers, researchers, and technical professionals working with or interested in LLM serving infrastructure. If you're deploying models in production, evaluating serving solutions, or researching optimization techniques, this course provides the deep technical knowledge you need.

You should have basic familiarity with LLMs (transformer architecture, inference vs. training), Python programming, and cloud/distributed systems concepts. Prior experience with serving frameworks (TensorFlow Serving, TorchServe, etc.) is helpful but not required. This is an intermediate to advanced course, not for absolute beginners.

While the course is based on cutting-edge research (including the DistServe paper and UCSD Hao AI Lab retrospective), it provides structured learning with interactive diagrams, hands-on exercises, quizzes, and real-world case studies. You'll get curated, synthesized knowledge instead of spending weeks piecing together scattered resources—saving you 20+ hours of research time.

Absolutely. Disaggregated inference is rapidly becoming industry standard at companies like Fireworks, Perplexity, and DeepSeek. Being able to discuss prefill-decode separation, KV-cache optimization, and interference elimination signals deep technical expertise. The certificate demonstrates your commitment to staying current with production ML systems.

Many students successfully expense the course as professional development or training. We provide a detailed receipt with course description, learning objectives, and total hours that you can submit to your employer. At $249, it's significantly more cost-effective than traditional training programs ($2,000+) or conferences.

Most students complete the course in 6-10 hours spread over 1-2 weeks, though you can go faster or slower based on your schedule. The 6 modules are self-paced: Module 1 (30 min), Module 2 (45 min), Module 3 (2-3 hours), Module 4 (1 hour), Module 5 (3-4 hours), Module 6 (15 min). You have lifetime access, so there's no rush.

Yes! Your one-time payment includes all future updates, new modules, additional exercises, and content additions at no extra cost. As LLM serving techniques evolve (new frameworks, optimization methods, research papers), we'll update the course to keep you current. Once enrolled, you're always enrolled.

The course includes both conceptual learning and practical application. Module 5 features 5 hands-on activities: creating serving diagrams, analyzing scheduling algorithms, designing disaggregated architectures, simulating workloads, and evaluating real-world systems. You'll build artifacts you can use in interviews or add to your portfolio.

We cover production frameworks used by leading AI companies: vLLM (PagedAttention, continuous batching), SGLang (RadixAttention), NVIDIA TensorRT-LLM, Ray Serve, and emerging tools like LMCache and MoonCake. The focus is on vendor-agnostic principles applicable across any serving stack, not just one tool.

Yes. You'll understand the architecture deeply enough to evaluate existing frameworks (vLLM, SGLang), design disaggregated systems for your use case, make informed build-vs-buy decisions, and contribute to open-source projects. The course bridges research and implementation with real-world deployment considerations.

Absolutely. The course is based on the "Disaggregated Inference: 18 Months Later" retrospective (published December 2024) covering the evolution from initial resistance to rapid industry adoption. We include 2024-2025 developments: Splitwise (Attention-FFN disaggregation), TetriInfer, DeepSeek-V3, NVIDIA Rubin, and production deployments at Fireworks/Perplexity.

Yes! The Certificate of Completion is designed to be shared on LinkedIn (Education or Licenses & Certifications sections), your resume, portfolio, or personal website. It includes your name, completion date, course title, and credential details. Many students report positive engagement from recruiters and colleagues after posting their certificate.

Yes! Enrolled students get access to our course community where you can ask questions, share insights, discuss implementation challenges, and connect with fellow ML engineers and researchers. Module 6 also provides curated links to active communities (vLLM Discord, Reddit r/MachineLearning, Twitter hashtags) where you can engage with the broader ecosystem.

Absolutely! We encourage team learning. Each team member needs their own enrollment ($249/person) for certificate issuance and progress tracking. For teams of 5+, contact us for potential volume discounts. Group learning is powerful—teams can discuss concepts together and align on serving architecture decisions.

University courses cost $2,000-5,000+, require fixed schedules, and often lack production focus (emphasizing theory over real-world deployment). Our course costs $249, offers lifetime access at your own pace, and is taught by practitioners based on systems running at scale. You get production-ready knowledge in a fraction of the time and cost.

Each module includes detailed explanations, diagrams, and examples designed to be self-explanatory. For additional questions, you can engage with the course community, explore the 36+ curated resources in Module 6 (academic papers, GitHub repos, communities), or contact course support. The content is structured to minimize confusion and maximize clarity.