Use Case Patterns for LLM Applications (1).pdf

This session was presented at the AWS Community Day in Munich (September 2023). It's for builders that heard the buzz about Generative AI but can’t quite grok it yet. Useful if you are eager to connect the dots on the Generative AI terminology and get a fast start for you to explore further and navigate the space. This session is largely product agnostic and meant to give you the fundamentals to get started.

LLM, ChatGPT, AI. A take on the new wave! After the PC, the internet, mobile and the cloud, the new wave is here: AI

In this episode we'll discuss the different flavors of prompt engineering in the LLM/GPT space. According to your skill level you should be able to pick up at any of the following: Leveling up with GPT 1: Use ChatGPT / GPT Powered Apps 2: Become a Prompt Engineer on ChatGPT/GPT 3: Use GPT API with NoCode Automation, App Builders 4: Create Workflows to Automate Tasks with NoCode 5: Use GPT API with Code, make your own APIs 6: Create Workflows to Automate Tasks with Code 7: Use GPT API with your Data / a Framework 8: Use GPT API with your Data / a Framework to Make your own APIs 9: Create Workflows to Automate Tasks with your Data /a Framework 10: Use Another LLM API other than GPT (Cohere, HuggingFace) 11: Use open source LLM models on your computer 12: Finetune / Build your own models Series: Using AI / ChatGPT at Work - GPT Automation Are you a small business owner or web developer interested in leveraging the power of GPT (Generative Pretrained Transformer) technology to enhance your business processes? If so, Join us for a series of events focused on using GPT in business. Whether you're a small business owner or a web developer, you'll learn how to leverage GPT to improve your workflow and provide better services to your customers.

Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities? This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.

In this event we will cover: - What is Generative AI and how it is being for future of work. - Best practices for developing and deploying generative AI based models in productions. - Future of Generative AI, how generative AI is expected to evolve in the coming years.

This document provides an overview of building, evaluating, and optimizing a RAG (Retrieve-and-Generate) conversational agent for production. It discusses setting up the development environment, prototyping the initial system, addressing challenges when moving to production like latency, costs, and quality issues. It also covers approaches for systematically evaluating the system, including using LLMs as judges, and experimenting and optimizing components like retrieval and generation through configuration tuning, model fine-tuning, and customizing the pipeline.

The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.

The presentation "ITDays_2023_GeorgeBara" discusses challenges in adopting AI large language models (LLMs) in enterprise settings. The presentation covers: 1. **Challenges in AI LLMs adoption**: It highlights the noise in the current AI landscape and questions the practical use of AI in real businesses. 2. **The DNA of an Enterprise**: Defines enterprise sizes and discusses the new solutions adoption process, emphasizing effective integration and minimizing disruption. 3. **Enterprise-Grade**: Lists qualities like robustness, reliability, scalability, performance, security, and support that are essential for enterprise-grade solutions. 4. **What are LLMs?**: Describes the pre-ChatGPT era with BERT, a model used for language understanding, and details its enterprise applications. 5. **LLM use-cases before ChatGPT**: Focuses on data triage, process automation, knowledge management, and the augmentation of business operations. 6. **EU Digital Decade Report**: Points out that AI adoption in Europe is slow and might not meet the 2030 targets. 7. **Adoption Challenges**: Addresses top challenges such as data security, predictability, performance, control, regulatory compliance, ethics, sustainability, and ROI. 8. **Conclusion**: Reflects on the slow adoption of AI in enterprises, suggesting that a surge might occur once the technology matures and is ready for enterprise use. The presenter concludes by stating that despite the hype around technologies like ChatGPT, enterprises are cautious and will adopt new technologies at their own pace. He anticipates a gradual then sudden adoption pattern once LLMs are proven to be enterprise-ready.

Discussion of the current state of generative AI/Large Language Model technology, exploration of whether chat AIs can really think, future projections

Generative AI: Past, Present, and Future – A Practitioner's Perspective As the academic realm grapples with the profound implications of generative AI and related applications like ChatGPT, I will present a grounded view from my experience as a practitioner. Starting with the origins of neural networks in the fields of logic, psychology, and computer science, I trace its history and align it within the wider context of the pursuit of artificial intelligence. This perspective will also draw parallels with historical developments in psychology. Against this backdrop, I chart a proposed trajectory for the future. Finally, I provide actionable insights for both academics and enterprising individuals in the field.

The document discusses AI tools for software testing such as ChatGPT, Github Copilot, and Applitools Visual AI. It provides an overview of each tool and how they can help with testing tasks like test automation, debugging, and handling dynamic content. The document also covers potential challenges with AI like data privacy issues and tools having superficial knowledge. It emphasizes that AI should be used as an assistance to humans rather than replacing them and that finding the right balance and application of tools is important.

Thank you for the detailed review of the protein bars. I'm glad to hear you and your family are enjoying them as a healthy snack and meal replacement option. A couple suggestions based on your feedback: - For future orders, you may want to check the expiration dates to help avoid any dried out bars towards the end of the box. Freshness is key to maintaining the moist texture. - When introducing someone new to the bars, selecting one in-person if possible allows checking the flexibility as an indicator it's moist inside. This could help avoid a disappointing first impression from a dry sample. - Storing opened boxes in an airtight container in the fridge may help extend the freshness even further when you can't

Join Dr. Greg Loughnane and Chris Alexiuk in this exciting webinar to learn all about the tooling, processes, and team structure you need to build and operate performant, reliable, and scalable production-grade LLM applications!

Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.

Prompt engineering is a technique in artificial intelligence to get AI models like ChatGPT to respond correctly to our needs. The 5W1H framework can be used to get good results from ChatGPT by structuring prompts around what, who, why, where, which, and how. Prompts should provide context on what is expected from the AI, who the context is for, why the generated content is needed, where it will be used, which additional information is required, and how the output should be formatted. Well-structured prompts using this framework can elicit high-quality responses from ChatGPT.

With the recent buzz on Generative AI & Large Language Models, the question is to what extent can these technologies be applied at work or when you're studying and how easy is it to manage/develop your own models? Hear from our guest speaker from Google as he shares some insights into how industries are evolving with these trends and what are some of Google's offerings from Duet AI in Google Workspace to the GenAI App Builder on Google Cloud.

This presentation presents an overview of the challenges and opportunities of generative artificial intelligence in Web3. It includes a brief research history of generative AI as well as some of its immediate applications in Web3.

This document discusses challenges and solutions for machine learning at scale. It begins by describing how machine learning is used in enterprises for business monitoring, optimization, and data monetization. It then covers the machine learning lifecycle from identifying business questions to model deployment. Key topics discussed include modeling approaches, model evolution, standardization, governance, serving models at scale using systems like TensorFlow Serving and Flink, working with data lakes, using notebooks for development, and machine learning with Apache Spark/MLlib.

Behind the growing interest in Generate AI and LLM-based enterprise applications lies an expanded set of requirements for data integrations and ML orchestration. Enterprises want to use proprietary data to power LLM-based applications that create new business value, but they face challenges in moving beyond experimentation. The pipelines that power these models need to run reliably at scale, bringing together data from many sources and reacting continuously to changing conditions. This talk focuses on the design patterns for using Apache Airflow to support LLM applications created using private enterprise data. We’ll go through a real-world example of what this looks like, as well as a proposal to improve Airflow and to add additional Airflow Providers to make it easier to interact with LLMs such as the ones from OpenAI (such as GPT4) and the ones on HuggingFace, while working with both structured and unstructured data. In short, this shows how these Airflow patterns enable reliable, traceable, and scalable LLM applications within the enterprise. https://airflowsummit.org/sessions/2023/keynote-llm/

Helixa uses serverless machine learning architectures to power an audience intelligence platform. It ingests large datasets and uses machine learning models to provide insights. Helixa's machine learning system is built on AWS serverless services like Lambda, Glue, Athena and S3. It features a data lake for storage, a feature store for preprocessed data, and uses techniques like map-reduce to parallelize tasks. Helixa aims to build scalable and cost-effective machine learning pipelines without having to manage servers.

RightScale Webinar: January 13, 2011 – Watch this webinar for a look behind the scenes as we discuss ServerTemplates and how are they different from alternate approaches.

"We can all agree that streaming is super cool. And for a while now, the adoption conversation has been largely led with an all-in mentality. But that’s silly. The only concerns end users have are: -The freshness of their data -Latency they require to meet their SLAs from source to consumption -All while maintaining data quality and governance. Luckily, the industry has realized this and we have seen a shift of streaming capabilities surfacing as an in-database technology, via objects as trivial to analytics engineers as views - materialized that is. With this convergence of streaming capabilities and batch level accessibility, this is when ELT tools like dbt can join in and expand out the adoption story. dbt is the T in ELT, Extract Load and Transform. In dbt, analytics engineers design models - SQL (and occasional python) statements that encapsulate business logic. At runtime, dbt will wrap that logic in a DDL statement and send it over to the data platform to execute. In this session, we’ll discuss how we see streaming at dbt Labs. We will dive into how we are extending dbt to support low-latency scenarios and the recent additions we have made to make batch and streaming allies in a DAG rather than archenemies."

"Cloud" computing provides significant advantages and enormous cost savings by allowing IT infrastructure to be provisioned as a ubiquitous, metered, unit priced and on demand service. However, the other major resourcing issue faced by CIO’s is the provision of skilled labour to develop, support and maintain a increasing wide range of IT applications. This session will show attendees how the worldwide pool of freelance developers, the "Crowd", can be utilised as a ubiquitous, metered, unit priced and on demand resource pool to work in the "Cloud" to improve responsiveness to customer demands, reduce development timeframes and achieve significant cost savings. Although the crowd can bring enormous benefits in terms of cost and agility, there are some technical and business barriers to adoption in large organisations. This presentation will discuss the barriers and, using some real examples, will explain how GoSource overcomes them.

I presented to the Georgia Southern Computer Science ACM group. Rather than one topic for 90 minutes, I decided to do an UnConference. I presented them a list of 8-9 topics, let them vote on what to talk about, then repeated. Each presentation was ~8 minutes, (Except Career) and was by no means an attempt to explain the full concept or technology. Only to wake up their interest.

A talk for SF big analytics meetup. Building, testing, deploying, monitoring and maintaining big data analytics services. http://hydrosphere.io/

Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment? How do I embed what I have learned into customer facing data applications? In this webinar, we will discuss best practices from Databricks on how our customers productionize machine learning models do a deep dive with actual customer case studies, show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.

Cloud Native Compute Foundation and KubeCon 2024 - Paris Cloud Native Artifical Intelligenet (CNAI)

Learn best practices for OLAP modeling using Cognos 8 Transformer. View the video recording and download this deck: http://www.senturus.com/resources/best-practices-in-olap-modeling-with-cognos-transformer/ Topics include: • The value of online analytical processing (OLAP) • Reference business intelligence architecture • How to synchronize Transformer and the data source • Customizing the Transformer model using sub-dimensions and relative time categories • Resolving conflicts when using multiple data sources • Setting measure properties and comparing roll-up options plus applying basic cube security • OLAP enhancements in Cognos 10 Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.

Are Open LLMs useful for production applications, or are they low quality toys useful only for experiments? We share our experiences using open LLMs vs proprietary LLMs.

The document provides guidance on designing a complex web application by breaking it into multiple microservices or applications. It recommends asking questions about team size, traffic patterns, priorities for speed vs stability, existing APIs or libraries, and programming languages. Based on the answers, it suggests appropriate frameworks, languages, data storage, testing/deployment processes, and server/container management options. The overall goal is to modularize the application, leverage existing tools when possible, and not overengineer parts of the design.

The document discusses Retrievable Augmented Generation (RAG), a technique to improve responses from large language models by providing additional context from external knowledge sources. It outlines challenges with current language models providing inconsistent responses and lack of understanding. As a solution, it proposes fine-tuning models using RAG and additional context. It then provides an example of implementing a RAG pipeline to power a question answering system for Munich Airport, describing components needed and hosting options for large language models.

Explore the use-cases and architecture for Apache Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.

A whitepaper is about Qubole on AWS provides end-to-end data lake services such as AWS infrastructure management, data management, continuous data engineering, analytics, & ML with zero administration https://www.qubole.com/resources/white-papers/qubole-on-aws

A workshop held in StartIT as part of Catena Media learning sessions. We aim to dispel the notion that large PHP applications tend to be sluggish, resource-intensive and slow compared to what the likes of Python, Erlang or even Node can do. The issue is not with optimising PHP internals - it's the lack of proper introspection tools and getting them into our every day workflow that counts! In this workshop we will talk about our struggles with whipping PHP Applications into shape, as well as work together on some of the more interesting examples of CPU or IO drain.

Generative AI Bootcamp

The document provides information about an experienced machine learning solutions architect. It includes details about their experience and qualifications, including 12 AWS certifications and over 6 years of AWS experience. It also discusses their vision for MLOps and experience producing machine learning models at scale. Their role at Inawisdom as a principal solutions architect and head of practice is mentioned.