Shubham Saboo

Semantic Search with Low-Code

Tenstorrent

Jina AI

Arcesium

AI Author, Product Manager at Tenstorrent, Founder of Unwind AI

Create a powerful RAG pipeline for semantic search with a few lines of Python code

Build RAG pipeline for semantic search

Ingest data from multiple sources into a vector database with minimal Python code

Integrate data from multiple sources

Setup and deploy a semantic search pipeline end-to-end using Embedchain and Streamlit

Deploy your application in a few clicks

👋 Welcome to your next great LLM adventure! 🏗️ 🚢🚀 We invite you to join the close-knit AI Makerspace community and start building, shipping, and sharing with us! 🤩 Each cohort culminates in a unique live Demo Day experience! https://bit.ly/demo-day-shorts 🧑‍💻 You will learn to prototype LLM applications according to best practices, using the four primary patterns of Generative AI as first principles: Prompt Engineering, Retrieval Augmented Generation (RAG), Fine-Tuning (of LLMs and embedding models), and Agents. https://bit.ly/rag-and-agents 🧑‍🤝‍🧑 You will get hands-on building and sharing complex LLM applications weekly, receiving constant feedback during live sessions and through your homework submissions via instructors, peer supporters, and your fellow cohort members. 📇 RAG systems are front and center in the course. You'll learn how to build applications from quick prototypes with OpenAI GPT models to scalable production applications with the latest open-source LLMs and embedding models. ☁️ While never getting locked into a single cloud-computing vendor, you'll orchestrate systems from scratch in Python and using core version-controlled LangChain constructs, the leading framework for building production LLM applications. https://bit.ly/learn-langchain 🧐 You will outfit your applications with visibility and monitoring tooling from LangSmith, and you'll learn to evaluate your applications' inputs, outputs, and usage! 🚀 Optimize your prompting with DSPy, your RAG applications with advanced retrieval and chunking, and your LLM inference and serving with advanced quantization techniques like AWQ and state-of-the-art libraries like vLLM! https://bit.ly/llms-in-prodhttps://bit.ly/llm-edge-toolkit -- **🙋 What are the prerequisites? All applicants must complete THE AI ENGINEERING BOOTCAMP CHALLENGE. This will require you to set up your LLM application development environment and deploy your first LLM application! aimakerspace.journey.io/p/aie-challenge **🙋 How will I know if it's right for me? If you're unsure if this could be a fit for you, you'll find tons of resources on this page to help you decide, including: 📜 Curriculum📅 Detailed Schedule🤩 Demo Day Videos🎤 Student Interview Videos You can also start by reading Richard's experience over the 10 weeks he spent in Cohort 3! https://bit.ly/aie-cohort-3-reflections **💰 How much do the tools cost? Don't worry! This course is for the GPU-poor among us! This course adapts to industry-standard open-source software and compute hardware and is cloud platform agnostic. We strive to keep your additional costs low (below ~$100 total). Check Q&amp;A for more info!**

Prototype LLM apps and get them into Production.  Ship fast, then scale.

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

<ul><li>W1: Jan 14th, 16th</li><li>W2: Jan 21st, 23rd</li><li>W3: Jan 28th, 30th</li><li>W4: Feb 4th, 6th</li><li>W5: Feb 11th, 13th</li><li>W6: Feb 18th, 20th</li><li>W7: Feb 25th, 27th</li><li>W8: Mar 4th, 6th</li><li>W9: Mar 11th, 13th</li><li>W10: Mar 18th, 20th</li></ul> * All sessions are recorded!

Tuesdays & Thursdays

Each class period, we will get hands-on with Python coding homework!

Weekly Programming Projects

Open to the public - Friends, family, coworkers, and bosses welcome! <a href="https://www.youtube.com/playlist?list=PLrSHiQgy4VjGf2r-0n__jMDYtiLOf6OPo" rel="noopener noreferrer" target="_blank">AIE1 Demo Day Playlist</a> &amp; <a href="https://www.linkedin.com/events/7177388473547468800/comments/" rel="noopener noreferrer" target="_blank">LinkedIn Event</a><a href="https://www.youtube.com/playlist?list=PLrSHiQgy4VjEmTW_5SYdSJT8xOKMtJC5b" rel="noopener noreferrer" target="_blank">AIE2 Demo Day Playlist</a> &amp; <a href="https://www.linkedin.com/events/7194472892556853248/comments/" rel="noopener noreferrer" target="_blank">LinkedIn Event</a><a href="https://www.youtube.com/playlist?list=PLrSHiQgy4VjEkgERc9palQ4W_LTM22UyN" rel="noopener noreferrer" target="_blank">AIE3 Demo Day Playlist</a> &amp; <a href="https://www.linkedin.com/events/7219879535939330049/comments/" rel="noopener noreferrer" target="_blank">LinkedIn Event</a>

Demo Day!

Dr. Greg: Thursdays @ 8 AM PTThe Wiz 🪄: Fridays @ 3 PM PT

Instructor Office Hours

We typically cover 6-7 days per week with 1 or more office hours held by peer supporters!

Peer Supporter Office Hours

Tuesdays at 9 AM PT: For International (EMEA) students, a Certified AI Engineer will hold weekly 60-minute deep dive sessions!

International-Friendly Weekly Deep Dive Sessions

Course schedule

Data scientists and machine learning engineers aiming at becoming full-stack AI practitioners.

Software engineers who want to build complex LLM applications.

Technical product managers who guide their teams on what to build, why, and how.

Who is this course for

What you’ll get out of this course

Student success will help you achieve your goals before and after certification!

🤝 1:1 Career Coaching

Certified AI Engineers get direct access to job opportunities from our network

💼 Hiring Opportunities

Work with certified AI Engineers in small groups to discuss and code every class!

🧑‍🤝‍🧑 Peer-Supported Live Coding

You will present a unique project live to a cohort of your peers and the public!

🤩 Demo Day!

Build Retrieval Augmented Generation applications and ground LLM outputs in your own reference data!

🙋 Retrieval Augmented Generation (RAG)

Build complex LLM applications capable of reasoning, action, and working with external tools!

🕴️ Agents

Leverage in-context learning as an engineer for prototyping, RAG applications, agents, and more!

🧑‍💻 Prompt Engineering

Adapt open-source embedding models to your domain-specific language or data!

🔢 Embedding Fine-Tuning

Efficiently train LLMs to perform specific tasks using techniques like PEFT-LoRA and quantization!

⚖️ LLM Fine-Tuning

Instrument LLM applications for quantitative metrics-driven development!

📈 Evaluation

Debug, test, and monitor applications built using any leading LLM framework!

🔎 Visibility

Ensure efficient and scalable inference using the right tools and techniques!

🚀 Inference & Serving

Build RAG & Agents w/ LangChain.  Fine-Tune Embeddings & Llama 3.1.  Deploy to Prod in the Cloud & On-Prem.  Monitor and Evaluate w/ LLM Ops

"Dr. Greg" Loughnane

Co-Founder & CEO @ AI Makerspace

Chris "The Wiz 🪄" Alexiuk

Co-Founder & CTO @ AI Makerspace

Self-paced cohort

AIE5

Become the RAG Product Thinker Your Company Needs In the rapidly evolving world of AI, companies are in a fierce arms race. They're scrambling to find developers and product leaders who can successfully incorporate AI into their products before competitors do it better. Are you ready to be that leader? The Challenge: Navigating the RAG Black Box You're working on AI-powered applications, but: * Time and resources are limited* The best path forward isn't clear* The inner workings of your product are shrouded in uncertainty The Solution: Develop the "Product Sense" for AI In just 6 weeks, learn to: * Optimize search quality and latency* Design robust feedback loops for continuous improvement* Implement data-driven strategies for maximum impact* Navigate AI product decisions in uncertain environments Course Breakdown: From Fundamentals to Advanced Techniques Week 1: Fundamentals and Synthetic Data Generation* Understand the importance of having a system for evaluating and improving RAG* Learn to generate synthetic data for fast evaluations* Master precision and recall metrics for retrieval evaluation* Distinguish between leading and lagging metrics* Implement the RAG System Inference Flywheel Week 2: Segmentation and AnalysisLearn the importance of segmenting queries and usersDifferentiate between inventory issues and capability issuesUse clustering and classification to identify query types and segmentsSet up dashboards to monitor query distributions and performance over timePrioritize improvements based on impact, volume, and likelihood of success Week 3: Structured Extraction and MultimodalityLearn techniques for handling different data types: documents, images, tablesExtract metadata and generate synthetic data/summaries to improve searchImplement approaches for document search, image search, and querying tablesApply Week 1-2 techniques to evaluate and improve individual indicesBalance trade-offs between generalized and specialized approaches in multimodal RAG Week 4: Query Routing and Tool SelectionCombine multiple search indices into a cohesive applicationImplement parallel function calling and API gateways to route queriesEvaluate tool selection as a classification taskAvoid data leakage when generating examplesBreak down probability of success into tool selection and retrieval components Week 5: Representations and Fine-tuningUnderstand limitations of pre-trained embedding modelsLearn the importance of fine-tuning embeddings and re-rankers on domain-specific dataCollect relevance data to create triplet training examplesExplore benefits of fine-tuning a single model across multiple tasksDiscover how relatively small amounts of data can lead to significant improvements Week 6: Product Design and User ExperienceImplement techniques for collecting user feedbackDesign streaming strategies to improve perceived latencyCreate UI components for rendering citations and follow-up actionsApply prompting techniques like chain-of-thought and monologuesAdd validators to improve reliability and response qualityDevelop strategies for handling negative examples Why This Course, Why Now?RAG is becoming essential for competitive AI integrationFocus is shifting from basic implementation to performance optimizationCore principles of effective RAG systems are crystallizingEarly adopters gain significant market advantages What You'll LearnCold start your evaluation pipeline for retrievalUnderstand the limitations of embedding models and how to think about rerankers and fine-tuningMaster retrieval metrics and use them to quickly run experimentsIdentify high-impact tasks and prioritize effectivelyMake informed tradeoffs and choose relevant metricsFocus on what matters most in AI product development Why Your Team Needs ThisAlign on RAG best practicesSave months of trial and errorBuild scalable systems that prevent future rewritesFoster a data-driven culture of continuous improvementBridge gaps between technical and business teamsGain competitive edge in AI implementationJustify AI investments to leadershipLearn from real-world case studies across industries About Your InstructorsJason Liu is a seasoned machine learning consultant with experience at Stitch Fix, Meta and dozens more. He has consulted with a large range of companies, from startups like Limitless to larger enterprises like Hubspot and Zapier.&nbsp; When companies struggle to make progress, they hire Jason to help their AI teams find "the path" forward. Jason is a machine learning engineer and data scientist with 8 years of experience in building recommendation systems and multi-modal semantic search products at Stitch Fix. What You'll GetA community of other operators and AI product thinkersHands-on experience with real-world RAG applicationsStrategies for continuous system improvementSkills applicable to all AI initiatives, not just RAGAccess to instructors' real-world experience and immediately applicable insightsBonus Offers (Over $1500 in Value!)$500 Cohere credits$200 LanceDB credits and free access to Lance Cloud$500 in Modal Labs credits6 months free Notion AI Plus3 months Braintrust access ($250 value) Who Should Apply?This course is perfect for you if:You're working on software products incorporating AIYou've built a RAG prototype and want to move to productionYou're looking to develop a "product sense" for AI applications Won't a lot change by February?Course focuses on enduring principles, not just current toolsYou'll learn to evaluate and integrate new technologies rapidlyStrategies taught focus on ongoing system optimizationBy February, you'll be positioned to leverage new developments immediatelyCourse content will be updated to reflect any significant changesSkills developed are foundational and will remain relevant Don't Miss Out!🚀 Limited Spots Available 🚀 Our small-group cohorts fill up fast. Don't wait to level up your RAG skills. Remember, you only pay after your application is approved. We're so confident in the value of this course that we offer a full refund if you don't see meaningful improvements in your processes within 5 weeks. Join a small cohort of other teams shipping real applications and take your RAG skills to the next level. Don't do it alone - be part of a community of professionals facing similar challenges and working towards AI excellence.

Acquire the skills & confidently improving and iterate on RAG applications

Workshops covering each step of the playbook and helping you build process improvements in your RAG application

Tuesday: Workshops

The first half hour will be interactive breakout sessions, and the closing half-hour each week is Q&amp;A

Wednesday: Office Hours + Breakout Sessions

Guest instructors covering key topics in both innovative theory and practical applications in RAG system development.

Thursday: Guest Speakers

Course Schedule Each Week

An Engineering or Product leader looking to improve an existing RAG system MVP

Solving problems like poor retrieval, unreliable outputs or unhappy customers with your existing application

Ready to lead your team in building a data flywheel so you can leverage feedback

This Course Is For You If You Are

By the end of this course, participants will be able to

Implement a systematic approach to developing and improving RAG applications using the Data and Evals Flywheel methodology.

Design and execute fast, unit test-like evaluations to assess retrieval capabilities, including precision and recall metrics.

Generate and utilize synthetic data for rapid evaluation and iteration of RAG systems.

Apply fine-tuning strategies for embedding models and implement hard negative mining techniques to enhance search relevance.

Classify different types of queries and conduct bottleneck analysis to identify performance limitations in RAG systems.

Differentiate between limited inventory and limited capabilities issues, and develop strategies to address both.

Design and implement specialized indices for various data types, including documents, images, tables, and SQL databases.

Apply synthetic text chunk generation and summarization techniques to improve retrieval performance across different modalities.

Develop efficient query routing systems and implement effective index fusion strategies for complex RAG setups.

Evaluate the performance of both query routing and individual indices separately to optimize overall system performance.

Design and integrate both explicit and implicit feedback mechanisms to drive continuous system improvement.

Follow a repeatable process to continually evaluate and improve your RAG application

Jason Liu

Cohort 1

Cohort 2

Welcome to the most technically rigorous and hands-on Large Language Model (LLM) application course available today. 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: - 6 weeks of in-depth content- 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

Sundays

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

Weekly projects

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:

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 3

Hamza Farooq

Cohort 4

Over 6 weeks, I will teach the essentials of AI through practical examples, from simple report automation to fine-tuning LLMs for unique use cases. Each weekly session will consist of hands-on examples (with code) that you can adapt to your projects and use cases. 📖 Course Overview: Acquiring a practical understanding of AI through building projects, lectures will center around specific use cases that students can leverage in developing their projects. Students learn the basics of AI, data engineering, machine learning, generative AI, prompt engineering, RAG, fine-tuning, and AI project management. 🎯 Learning Objectives1. Define key terms and concepts associated with AI2. Eﬀectively use AI tools to accelerate existing workflows3. Design and implement basic machine learning systems with Python4. Develop custom AI systems using OpenAI and Hugging Face APIs5. Identify which AI techniques are best suited to a particular problem ✅ Who should take this course?This course is designed for learners at the start of their AI engineering journey. While this can include many backgrounds, basic coding skills (&gt;1 year, preferably Python) are highly recommended. • IT Professionals seeking hands-on experience with AI• Software Engineers building with AI systems• Data scientists and ML Engineers new to LLM development and GenAI• Business Leaders and Product Managers leading AI initiatives and teams• Entrepreneurs building AI-native products and services ❌ Who should NOT take this course?Those who have less than 1 year of coding experience. 🧑‍💻 10 example projects we will build in this course... • Automated analytics report emailer• Gmail ETL data pipeline• Machine learning email classifier• PDF parser and summarizer• Document Question Answering chatbot• Structuring unstructured survey data• Semantic search over YouTube videos• Multimodal Desktop RAG system• Fine-tuning LLM response style• Fine-tuning LLM for tool use

Learn AI by Building It

Weekly live lectures with instructor and fellow cohort members.

Fridays

(Optional) reading and video content to prepare for the upcoming session.

Session Pre-work

(Optional) weekly homework to put your learnings into practice.

Technical professionals—Get hands-on experience with AI and build the practical skills you need to advance your career in tech.

Business leaders and product managers—Gain the technical foundation needed to&nbsp;effectively lead AI initiatives&nbsp;and teams.

Entrepreneurs—Learn essential AI and ML engineering skills for developing AI-native products and services.

Create AI systems using machine learning, prompt engineering, fine-tuning, and beyond. Build functional prototypes that demonstrate your AI solutions as POCs.

Build custom projects from start to finish.

Build a solid foundation in Python for AI. Learn to automate tasks, create scripts, and understand key Python concepts that power AI solutions, setting the stage for more advanced AI techniques.

Develop essential Python skills.

Explore the evolution of AI, from Software 1.0 to Software 3.0, and learn how modern AI technologies like LLMs, GenAI, and prompt engineering are transforming industries and shaping the future of intelligent systems.

Understand the AI landscape.

Master a 5-step AI project management framework to guide you from problem formulation to deployment. Learn to structure AI projects efficiently, ensuring successful implementation and measurable results.

Manage AI projects effectively.

This is not about theories and abstract ideas. Rather, the guiding learning principle is to learn by building.

Lightning Lessons

Semantic Search with Low-Code