Run On Cloud

DeepSeek-R1 on AWS Kubernetes Service EKS with Ray and vLLM

Spryker Systems

Deutsche Bank

Autohero Deutschland

OneCloud

Aymen Segni

Staff Engineer, SRE, DevOps and Platform Engineering

Master the deployment of large GenAI models on AWS EKS by leveraging Ray and vLLM

DeepSeek-R1 Deployment on Amazon Elastic Kubernetes Service

Gain deep insights into optimizing GPU resources for high-performance  critical for handling 8B parameter model

GPU Optimization and Resource Management

Integrate AI endpoints into Kubernetes, monitor real-time performance, and ensure high availability.

End-to-End Integration & Monitoring

Acquire hands-on experience with Terraform, Kubernetes, Karpenter, and Grafana to build prod-grade GenAI Platforms

Use the DevOps and Cloud Native standard tools with GenAI

Understand how self-hosting GenAI models is cost-effective and ensures data security, Dev freedom, and top performance

Understand the Benefits of Self-Hosting your GenAI models

Welcome to today's most technically rigorous and hands-on Large Language Model (LLM) application course. This course is unique because each class is taught online and held with separate office hours - in total, we will conduct 15 sessions. Most courses are taught asynchronously, i.e., recorded sessions with Q&amp;A. We believe you should get what you pay for. There are 7 - 2 hr sessions for teaching and 7 office hours—1 session for DEMO DAY. We have increased our cohort to 10 weeks to allow you to learn and implement your projects thoroughly. This isn't just another AI course – it's your gateway to mastering the art and science of deploying production-grade LLM solutions that stand out in the real world. As part of Maven's Top-Rated Content, this course is designed for those who have already mastered the basics of RAG, cosine similarity, vector databases, and LLMs. We'll take you to the next level, focusing on practical aspects of packaging and deploying these models in real-world production environments. For cohort members joining this intensive learning experience, here's what you get: - 10 weeks of in-depth content in-person courses - We will teach you each class- Weekly office hours for personalized guidance- Real-world projects and challenging assignments- Guest Lectures by Leading AI Professionals- Continued support post-graduation- Lifetime access to course materials What You'll Master: Course Highlights Agents: Forget CrewAI or Autogen, build your won Agents from scratch. Learn what it takes to make an Agent from the ground. Contribute to Open Source community. Advanced RAG Solutions: Dive into enterprise-level RAG architectures and learn how to build and implement semantic caching from scratch using GCP and Redis. LLM Hosting and Deployment: Gain insights into best practices for hosting Large Language Models (LLMs) in diverse production settings, creating inference endpoints, and deploying LLMs on serverless platforms. Continual Pre-Training and Fine-Tuning: Explore advanced techniques for continual pre-training, fine-tuning LLMs, and mitigating catastrophic forgetting. Learn how to build a data pipeline for pre-training, apply causal language modeling, and leverage scaling laws. Model Merging and Mixture of Experts: Master techniques for merging multiple models to enhance their collective capabilities, including the Mixture of Experts (MoE) approach. Learn to use tools like mergekit for efficient model merging. Quantization Methods: Discover techniques to reduce model size while maintaining performance, crucial for deployment in resource-constrained environments. Inference Speed Optimization: Learn strategies to accelerate inference speeds for real-time language processing, ensuring efficient and responsive AI systems. Responsible AI Implementation: Explore ethical AI development using guardrails like NeMo, Colang, and Llama Guard to ensure AI systems align with responsible AI principles. Agentic RAG and Chunking Strategies: Implement advanced semantic chunking techniques and explore AI agent frameworks like AutoGen to enhance the capabilities of RAG systems. DSPy and Knowledge Graphs: Learn to create and utilize knowledge graphs effectively, mastering DSPy as an alternative prompting approach for structured data handling and enhanced AI interaction. Throughout the course, we will analyze state-of-the-art AI products, reverse-engineering some through Python. As a bonus, you'll have access to experimental products being developed at Traversaal.ai, my startup, allowing you to stay at the forefront of advancements in the field. Prerequisite: You should have coding experience of building RAG Solution. Understanding of Encoders and Decoders and some knowledge of Cloud Solutions and APIs. If you feel the need for a more foundational course, consider checking out my other offering on LLMs: Building LLM Applications (https://maven.com/boring-bot/ml-system-design).

Go Beyond Basic Frameworks: Build and Deploy Production-Grade AI Solutions

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

Sundays

Work in teams to build solutions, this requires engagement with other team members

Weekly projects

Course schedule

Machine Learning Engineer exploring different techniques to scale LLM solutions

Researcher, who would like to delve in to various aspects of open-source LLMs

Software Engineer, looking to learn how to integrate AI into their products

This course is for you if you are a:

What you’ll get out of this course

Understand and implement complex AI architectures, including enterprise-level RAG systems and agentic RAG strategies. You will also dive deep into the Mixture of Experts (MoE) technique and other model merging strategies to enhance the capabilities of your AI systems.

Advanced AI Architectures

From building semantic caches using GCP and Redis to deploying LLMs on serverless platforms like AWS Bedrock, you'll learn the practical skills to deploy and manage AI applications in real-world scenarios.&nbsp;

Practical Skills for Deployment

Acquire advanced techniques for fine-tuning LLMs, enabling you to adapt these models to specific tasks or domains and enhance their performance in targeted applications.

Fine-Tuning Expertise

Explore strategies for exploring and optimizing inference speeds, ensuring that your language models perform efficiently in real-time scenarios, a crucial skill for deploying responsive and scalable applications.

Efficient Inference Processing

Understand the importance of ethical AI development and learn to implement guardrails using tools like NeMo, Colang, and Llama Guard to ensure your AI systems align with responsible AI principles.

Knowledge of Responsible AI

Build and Optimize Production-Grade RAG and LLM Applications: Master Advanced Techniques for Scalable, Secure, and Low-Latency AI Solutions

Hamza Farooq

Cohort 5

2025-cohort-2

If you’re a software engineer or data scientist, chances are you’ve built or seen a proof of concept (POC) for a generative AI app. It’s exciting at first—an impressive demo that showcases the potential of large language models. But here’s the problem: that’s where it usually stops. These POCs often fail to scale into reliable, production-ready applications. The result? Endless iteration cycles, unreliable outputs, and frustration as teams struggle in what we call “POC purgatory.” It doesn’t have to be this way. With the right principles, processes, and workflows, you can build LLM applications that work in production – without overcomplicating pipelines or chasing trends. This course focuses on the iterative development, evaluation, and debugging practices that make AI systems production-ready. We’ll explore tools like RAG, embeddings, and agents, but the emphasis is on building scalable, robust systems – ensuring you leave with the mindset and skills to adapt as the technology evolves. You’ll not only learn how to design, test, and deploy generative AI applications, but also how to iterate and improve rapidly. This isn’t just about building something cool – it’s about creating AI systems that solve real problems, deliver real value, and scale with confidence. What to ExpectThis is a highly interactive course designed to get you results. Across live workshops, discussions, and hands-on projects, you’ll build your skills while solving real-world challenges. •	Live coding sessions to design and refine LLM applications.•	Iteration exercises to explore how small changes can dramatically improve reliability.•	Discussions on user-centric development and integrating feedback loops.•	Expert-led Q&amp;As to help you tackle your toughest challenges. What You’ll Learn •	How to transform POC demos into production-grade applications.•	Strategies for managing non-determinism and optimizing outputs through prompt engineering.•	How to monitor, log, and debug AI systems to ensure reliability and performance.•	Best practices for handling structured outputs and integrating function calling into your applications.•	Building workflows for iterative development and experimentation.•	Practical skills through hands-on app development, including creating a Generative AI app to query PDFs. Bonus Perks All students will receive - Receive $1,000 in Modal credits for cloud compute – giving you resources to power real AI applications.- $300 in Google Cloud and Gemini credits for the first 100 registrants.- $100 in Mistral Credits for the first 50 registrants.- 3-months of access to Learn Prompting Plus (valued at $117): Gain immediate access to over 20 hours of comprehensive courses in Prompt Engineering, Prompt Hacking, AI Image Generation, &amp; more!- More credits may be announced soon! Guest lectures Learn directly from leading practitioners solving real AI problems in production. These sessions focus on practical workflows, debugging strategies, and building scalable systems – not just theoretical research or cutting-edge multi-agent architectures. Enrolling will also give you access to all recorded guest lectures from Cohort 1 of this course: - Hamel Husain (Ex-Airbnb, GitHub) – Basic Data Literacy for Debugging and Evaluating LLMs&nbsp;- Swyx (Latent Space podcast, smol.ai, ai.engineer): Engineering AI Agents in 2025- Sander Schulhoff (CEO, LearnPrompting.org) – Prompt Engineering in the LLM SDLC&nbsp;- Charles Frye (Developer Advocate, Modal) – Hardware for LLM Developers- Ravin Kumar (DeepMind) – End-to-End LLM Product Development This Course Is For •	Data scientists and machine learning engineers who are sick of unreliable POCs and want to ship reliable LLM applications.•	Software engineers who want to learn how to build generative AI systems and master the LLM software development lifecycle. This Course Is Not For Those •	Looking for an introductory course in AI—some programming knowledge is required.•	Expecting ready-made solutions for AI problems without active participation.•&nbsp;Expecting deep dives into advanced evals, multi-agent systems, or large-scale RAG pipelines. This course emphasizes building and iterating on practical, production-ready systems.•	Unable to commit to hands-on projects, live discussions, and iterative development exercises. Why Take This Course By the end of this course, you’ll: •	Understand the first principles of generative AI development.•	Gain hands-on experience building robust, scalable applications.•	Learn how to troubleshoot, monitor, and optimize AI systems for production.•	Develop workflows to iterate and refine AI applications effectively.•	Leave with the confidence to deploy reliable AI systems at scale.

Ground principles that underpin successful GenAI app development

Live in person sessions. We'll finalize session times by mid-November.

Mondays and Wednesdays

To ensure hands-on practical time, there will be project work to complete throughout the course.

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

Who is this course for

You’ve seen impressive generative AI demos—but how do you take them to production? In this course, you’ll learn the exact workflows, tools, and engineering practices needed to turn concepts into reliable, scalable applications that deliver real value.

Move beyond POCs and build production-ready AI systems

Struggling with inconsistent outputs? Through interactive exercises, you’ll explore proven techniques for optimizing prompts and ensuring your AI delivers actionable results in production settings.

Master prompt engineering for reliable results

When your AI application breaks, you’ll know exactly how to fix it. We’ll walk you through real-world debugging sessions, monitoring techniques, and tools to ensure your systems stay reliable at scale.

 Learn to debug and monitor AI systems like a pro

Want your AI to produce precise, usable results? You’ll learn how to handle structured outputs and function calls, making your applications smarter and more user-friendly.

Build smarter applications with structured outputs

Stop wasting time in endless iteration cycles. Through guided projects, you’ll discover how to set up workflows that refine your applications faster and with less frustration.

Accelerate your development process with iterative workflows

Tired of theoretical knowledge? You’ll build a fully functional app that uses text and image models to query PDFs, giving you practical experience solving complex real-world problems.

Develop hands-on expertise by building GenAI and LLM apps

Whether you’re looking to lead AI projects, impress stakeholders, or level up your technical expertise, this course gives you the tools to stand out as an AI/ML practitioner.

Advance your career with practical, proven techniques

These aren’t just skills for demos—they’re skills for delivering scalable, production-ready systems that solve real-world business challenges.

Solve Real Problems at Scale

Leave the course with a project you can add to your portfolio, demonstrate to your boss, or use to showcase your ability to deploy reliable AI systems.

Showcase Tangible Results

Build LLM-powered software reliably & from first principles. Learn the GenAI software development lifecycle: agents, evals, iteration & more

Hugo Bowne-Anderson

Host of Vanishing Gradients podcast; ex-Outerbounds; expert in DS/ML.

Stefan Krawczyk

Ex-Stitch Fix; 13+ Years building / productionizing data, ML, and AI

Cohort 1

Cohort 2

Ready to level up your practical gen AI expertise?&nbsp;Forget the complexities and technical mumbo jumbo.&nbsp;Jump into the gen AI sandbox and give yourself permission to play.&nbsp;Transition from knowing your organizations agenda, to training your AI assistant.&nbsp;Explore, share, and elevate your gen AI skills in a supportive platform with your peers.&nbsp;It’s about the gentle nudge to turn your “should know” skills into “can do’s”, with a dose of fun and optimism.

Course Description

If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students

Mon & Wed; optional Sat

Feel free to type out dates as your title as a way to communicate information about specific live sessions or other events.

May 7, 2022

Schedule items can also be used to convey commitments outside of specific time slots (like weekly projects or daily office hours).

Senior professionals and executives who want to level up practical gen AI capabilities with vetted, expert tips.

Leaders who know they should be using gen AI for a breadth of activities, but haven’t quite tackled them yet.&nbsp;

No coding knowledge required. No technical complexity.

Unlock 8 live sessions and 15+ Rapid Results Lessons (10-min video/exercises). Explore the “Resource Vault” brimming with unique frameworks, assessments, reviews, playbooks, cheat sheets and more.

Fast track your learning with AI First by Design proprietary live sessions, dynamic reinforcement lessons, and valuable, curated resources.

Begin by evaluating your skills maturity level and AI First Mindset. Equip yourself with table-stakes guardrails, prompting skills and automation workflows.&nbsp; Get hands-on and build custom prompting playbooks, GPTs, automated workflows and more.&nbsp;

Master essential Gen AI skills to boost your communication, productivity, and creativity.

Build your new AI sidekick / assistant.&nbsp;Personalize it to tackle repetitive tasks, summarize documents, and nail your tone of voice. Customize it to match your style and say hello to a more efficient, automated workflow.

Ready to redefine productivity?  Build your new, customized AI assistant.

Draft your personalized learning plan. Enjoy six months of monthly coaching calls following the course , a private peer network, and ongoing added resources. Elevate your skills, learn from and with your peers, and embrace growth.

Create your Future Ready Learning & Accountability Plan

Harness gen AI.  Turn your “should do’s” into “can do’s.” No tech headaches, just savvy techniques, vetted tools, & welcoming peers. 

Andrea Rasmussen

Gen-AI L.E.A.P. Cohort 1

Do you catch yourself asking any of the following questions while building AI applications? 1. How do I test applications when the outputs are stochastic and require subjective judgements? 2. If I change the prompt, how do I know I'm not breaking something else? 3. Where should I focus my engineering efforts? Do I need to test everything? 4. What if I have no data or customers, where do I start? 5. What metrics should I track? What tools should I use? Which models are best? 6. Can I automate testing and evaluation? If so, how do I trust it? If you aren't sure about the answers to these questions, this course is for you. ---WHAT TO EXPECT This course will provide you with hands-on experience. Get ready to sweat through exercises, code and data! We will meet two times a week for four weeks, with generous office hours (read below for course schedule). We will also hold office hours and host Discord community where you can communicate with us and each other. In return, you will be rewarded with skills that will set you apart from the competition by a wide margin. (see testimonials below). The first three weeks will be dedicated to learning about the processes and tools surrounding evals (including extremely challenging caes). The fourth week will be case studies from several companies we've helped with evals and guest speakers. All sessions will be recorded and available to students asynchronously. --- COURSE CONTENTYou will learn the following: - How to evaluate subjective outputs and dynamic scenarios like multi-turn conversations- How to generate synthetic data before you have any users.- How to conduct error analysis and use the results to prioritize your efforts- How to ensure evaluations are trusted and aligned with your goals- How to automate parts of your evaluation workflow.- How to select the right tools and infrastructure, including our favorite tools.- How you can create a data flywheel for continual improvement.- How to avoid common pitfalls while building evals ---FREE CREDITSEach student will get the following incentives and compute credits: - Modal (https://modal.com/): $1,000 ---GUEST SPEAKERS 1. Benjamin Clavié: Common RAG mistakes &amp; how to diagnose them (using a data-driven apporach).2. Isaac Flath: Building custom annotation tools for error analysis with FastHTML3. JJ Allaire: Inspect, a modern OSS eval toolset used by Anthropic, Groq and other leading companies.

Eliminate the guesswork of building AI applications with data-driven approaches.

Monday & Wednesday & Friday

Engineers building AI applications who have little experience with machine learning or data science.

Those interested in moving beyond vibe-checks to data driven measurements you can trust, even when outputs are stochastic or subjective.

Founders and leaders who are unsure of the failure modes of their AI applications and where to allocate resources.

Who this course is for

We've tried all of them so you don't have to.

Acquire the best tools for finding, diagnosing, and prioritizing AI errors.

And how to best leverage data when you do have users.

Learn how to bootstrap with synthetic data for testing before you have users

Data flywheels ensure you have examples to draw from for prompts, tests, and fine-tuning.

Create a data flywheel for your applications that guarantees your AI will improve over time.

How can you really trust LLM-as-a-judge? How should you design them? We will show you how. We will also show you how to refine prompts, generate metadata, and other tasks with the assistance of AI.

Automate parts of your AI evaluation with approaches that allow you to actually trust and rely on them.

We will show you approaches to discover all the ways AI is not performing

Ensure your AI is aligned to your preferences, tastes and judgement.

There are an infinite number of things you can try, tests you can write, and data you can look at. We will show you a data-driven process that helps you prioritize the most important problems so you can avoid wasting time and money.

Avoid common mistakes we've seen across 35+ AI implementations.

We will provide end-to-end exercises, examples and code to make sure you come away with the skills you need. We will NOT just throw a bunch of slides at you!

Hands-On Exercises, Examples and Code

Generous office hours ensures students can ask questions about their specific issues and interests.

Personalized Instruction

Learn proven approaches for quickly improving AI applications.  Build AI that works better than the competition, regardless of the use-case.

Lightning Lessons

DeepSeek-R1 on AWS Kubernetes Service EKS with Ray and vLLM