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The LLM Development Stack

Published by Pearson

Intermediate content levelIntermediate

Applications, Agents, Chatbots, and more

  • Explore the full LLM development stack.
  • Experience cutting-edge tools like Hugging Face, Ollama, LMStudio, LangSmith, and AWS Bedrock.
  • Go beyond the theory to gain insights into practical implementations including LLM management, monitoring, performance optimization, and agent-based automation for real-world AI applications.
  • Learn through in-depth demos and explanations.

This class provides a comprehensive, demo-driven exploration of LLM application development, covering everything from foundational tools like Google Colab and Hugging Face to advanced deployment platforms, including Ollama, LM Studio, and cloud-based services like AWS Bedrock and Google Vertex. Participants will learn how to build, optimize, and evaluate LLM-powered applications, with a strong focus on real-world implementation, agentic workflows (LangChain, LlamaIndex), and chatbot development platforms (Rasa, Mirascope).

What sets this class apart is its emphasis on practical development and performance optimization, featuring LLM observability and benchmarking tools like OpenAI Evals and LangSmith. Whether you're a developer, data scientist, or AI engineer, this class will equip you with the latest tools and techniques to build scalable, high-performing LLM applications for enterprise and consumer use.

What you’ll learn and how you can apply it

  • Develop and deploy LLM-powered applications using local and cloud-based platforms like Ollama, LM Studio, AWS Bedrock, and Google Vertex.
  • Implement and optimize agentic workflows with LangChain, LlamaIndex, and CrewAI to create intelligent, autonomous AI systems.
  • Evaluate and enhance LLM performance using benchmarking and observability tools like OpenAI Eval, Helicone, LangSmith, and vLLM.
  • Build and integrate chatbot solutions with Rasa, Botkit, and Mirascope for scalable, real-world conversational AI experiences.

This live event is for you because...

  • You want to stay ahead in AI development by learning cutting-edge LLM tools and frameworks for both cloud and local deployment.
  • You need practical, hands-on experience with LLM fine-tuning, agentic workflows, and chatbot development using industry-leading platforms like Ollama, LangChain, and Rasa.
  • You’re looking to optimize and scale LLM applications with performance benchmarking, observability, and cost-efficient inference tools like OpenAI Eval, Helicone, LangSmith, and vLLM.
  • You want to bridge the gap between research and real-world AI applications, gaining the skills to build, test, and deploy production-ready LLMs.

Prerequisites

  • Basic knowledge of LLMs
  • Some knowledge of Python and Jupyter Notebook is useful
  • Some experience with GenAI chatbots such as ChatGPT, Gemini, Claude, etc.

Course Set-up

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Lesson 1: LLM Development Foundational Tools (30m)

  • Note: section is a combination of demo and slides
  • Google Colab
  • Huggingface
  • Q&A

Lesson 2: LLM Management Platforms (50m)

  • Overview of Ollama and LMStudio
  • Ollama Deep dive
  • Developing Gen AI apos using Ollama and Open WebUI
  • Q&A

Lesson 3: Agentic AI Development Tools (50m)

  • Agentic frameworks: LangChain/LangGraph, CrewAI, AutoGen
  • Automating agents with LangChain
  • Programming your own agent with Python
  • Q&A

Lesson 4: Agentic AI Development Protocols (30m)

  • Agentic Protocols
  • Message Chain Protocol (MCP)
  • Agent to Agent (A2A)
  • Model Context Protocol (MCP)
  • Q&A

Lesson 5: Cloud-based LLM Development Platforms (30m)

  • Overview of Cloud LLM development tools (Google Vertex, Azure AI Studio, AWS Bedrock)
  • AWS Bedrock deep dive
  • Building an AI Assistant with a Bedrock backend
  • Q&A

Lesson 6: LLM Monitoring and Observability (30m)

  • LLM Monitoring and Observability Overview
  • LangSmith
  • Q&A

Final Q&A and Wrap Up (20m)

Your Instructors

  • Rob Barton

    Rob Barton is a Distinguished Engineer with Cisco. Rob has worked in the IT industry for over 27 years, the last 25 of which have been with Cisco. Rob Graduated from the University of British Columbia with a degree in Engineering Physics. Rob is a published author, with titles on subjects of Generative AI, Quality of Service (QoS), Wireless Communications, and IoT. Additionally, he has co-authored many peer-reviewed research papers and leads Cisco’s academic research partnership program. Rob holds numerous patents in the areas of AI, wireless communications, network security, cloud networking, and IoT. His current areas of work include network automation and Agentic models for IT management.

  • Jerome Henry

    Jerome Henry is a Distinguished Engineer in the Office of the Wireless CTO at Cisco Systems. His main field of research is around optimization of performances in unlicensed wireless networks, which includes aspects of QoS, IoT, privacy, indoor location, but also AI/Machine Learning and LLMs centered on network languages. Jerome has more than 25 years of experience teaching technical courses in more than 15 different countries and 4 different languages, to audiences ranging from graduate degree students to networking professionals and technical support engineers. Jerome joined Cisco in 2012. Before that time, he was consulting and teaching heterogeneous networks and wireless integration with the European Airespace team, which was later acquired by Cisco to become their main wireless solution.

Skill covered

MLOps