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

Cohort 5

Hamza Farooq

Cohort 4

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.- 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

The best guide for leaders to implement Generative AI in their businesses with real results. What You'll Gain: Strategic Clarity:&nbsp;Understand the transformative power of generative AI and how it can reshape your industry and business models. Actionable Insights:&nbsp;Learn how to identify and evaluate high-impact opportunities for implementing generative AI across your organization. Practical Skills:&nbsp;Develop the skills to lead AI initiatives,&nbsp;make informed technology investments,&nbsp;and mitigate associated risks. Competitive Edge:&nbsp;Gain the knowledge to leverage generative AI for innovation,&nbsp;efficiency,&nbsp;and growth,positioning your company ahead of the curve. ﻿Confident Leadership:&nbsp;Become a champion for AI adoption,&nbsp;empowering your teams to harness its potential for success. Key Focus Areas:﻿* Demystifying Generative AI:&nbsp;Understanding the core concepts,&nbsp;capabilities,&nbsp;and limitations of generative AI technologies. * Identifying Business Opportunities:&nbsp;Exploring real-world use cases and identifying high-potential areas for implementation within your organization. * Strategic Planning &amp; Execution:&nbsp;Developing a roadmap for AI adoption,&nbsp;including project selection,&nbsp;resource allocation,&nbsp;and change management strategies. * Risk Management &amp; Ethical Considerations:&nbsp;Understanding the potential challenges and ethical considerations associated with generative AI and developing strategies to address them. * Future-proofing Your Business:&nbsp;Staying ahead of the curve by anticipating advancements in generative AI and their implications for your industry. By the end of this course, you'll be equipped with the knowledge and tools to confidently lead your organization into the era of generative AI. All sessions will be recorded and made available for you to keep immediately after the course concludes. Interested in an enterprise license or NGO sponsorship so your team can take the course? Please reach out directly to amplify@ayeshakhanna.com

Amplify your potential with AI

You will have access to all lessons once the cohort starts and can view them on your own pace.

42 Course Lessons

<ul><li>Course Orientation (1h, 1st week)</li><li>Final Remarks and Q&amp;A (1h, 3rd week)</li></ul>

Q&A with Dr. Ayesha Khanna

Senior Leadership (C-Suite and VP Level):Identify new business opportunities,&nbsp;optimize existing processes, make informed tech investments.

Department Heads &amp; Managers: (e.g., Marketing, Sales, Operations, HR, Finance) streamline workflows,&nbsp;boost efficiency,&nbsp;and improve decisions

Startup founders &amp; CEOs enhance business,&nbsp;from idea generation to solution building to customer service.

Understand the fundamentals of Generative AI, and how it is being used in different industries to increase productivity and profitability.

How Generative AI is different from existing AI techniques and why it is transformative for businesses


Identify AI opportunities by understanding your business needs,&nbsp;exploring use cases,evaluating feasibility, starting small ,and fostering AI at scale.

How to assess and identify business opportunities to leverage Generative AI.


Create an agile Generative AI roadmap fueled by data,&nbsp;collect information from diverse sources to tailor the Gen AI to your needs, identify priorities, estimate development efforts, and guide iterative development.

How to use data to build an agile roadmap for a Generative AI project


Get a practical roadmap for executing a Generative AI project,&nbsp;from ideation to deployment and learn how to overcome typical challenges and avoid common pitfalls.

How to run a Generative AI project lifecycle including choosing vendors and a team


Focus on business metrics that matter such as efficiency gains (time saved,&nbsp;cost reduction,output increase), quality improvements (accuracy, novelty, customer satisfaction), business outcomes (revenue growth, market share), and user experience (adoption, satisfaction).

How to choose realistic metrics to track the impact of Generative AI projects.


Make AI decision-making processes understandable,&nbsp;ensure accountability by assigning clear responsibility for AI actions and outcomes ,and uphold data privacy by securely collecting, storing ,and using data in compliance with relevant regulations.

How to create an AI governance policy to manage the risks related to AI and data.


Learn how to successfully build Generative AI solutions that increase productivity and profitability, and make you stand out in your career.


Dr. Ayesha Khanna

Cohort 3

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. 

Lightning Lessons

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