Learn about the terms Generative AI, Large Language Models (LLMs),Foundation Models,RAG and agents what the challenges are, and how to deploy your first agent
Generative AI Large Language Models (LLMs) LLMOps Site Reliability Engineering (SRE) AgentsAI Engineering
Your path to scalable and reliable generative AI solutions and LLM(Ops) implementations powered by our Digital Highway approach.
Realizing the full potential of new AI solutions and transforming your organization to benefit from LLMs and other models can be challenging.
Let's go on a journey and learn how to turn your projects, products, and services into reliable digital solutions!
The following journey addresses engineers, product owners, decision-makers, and anyone who wants an overview of the best practices for implementing end-to-end data, model, and code pipelines and reliability engineering in the context of rolling out generative AI and LLM solutions in their organization. Throughout the journey, you will learn about various roles and stages of the data and AI product lifecycle and how to implement and run LLMs in your own environment. You will also learn about the Digital Highway for data and machine learning systems, our blueprint for reliable, scalable, and continuous delivery to generate sustainable value for data and AI products and services.
Module 1
Introduction to AI Engineering
Module 2
Reliably Implementing Your First Agent
Explore the implementation of an agent in business, emphasizing how to operate it and discussing monitoring and observability for agents.
LLMs LLMOps Agents Retrieval-Augmented Generation (RAG) Site Reliability Engineering (SRE) Agents
Module 3
Tech Guide: Digital Highway for LLMs
Check out our technical guide on implementing your Digital Highway for LLMs based on the example of a chatbot assistant running in a Kubernetes infrastructure that can be implemented on-premise or in your preferred cloud.
Tech Guide LLM Digital Highway MLOps LLMOps Kubernetes
Module 4
The Digital Highway for Machine Learning Systems
Discover the blueprint of a digital highway for machine learning systems, which incorporates all aspects above and visualizes an end-to-end approach for reliable and continuous machine learning delivery and operations.
ML(Ops) Lifecycle Quality Gates & Automation Incident & Change Management Data, Model & Code Pipelines Operational AI SystemsWebinar: 06 July 2023
The Digital Highway for End-to-End Machine Learning & Effective MLOps