Microsoft Prompt Engine, Creators of Intelligence & LLMs-Powered Apps with OPL Stack

Build Your Own ChatGPT-Like App with Streamlit                                                                                                                                                                                                                                                                                                                                                                                                                 [Open in app]( or [online]() [Microsoft Prompt Engine, Creators of Intelligence & LLMs-Powered Apps with OPL Stack]( Build Your Own ChatGPT-Like App with Streamlit Apr 7   [Save]( [▷  Listen](   👋 Hey, "The beauty of LLMs is that they can learn to do almost anything, but the challenge is to teach them to do what we actually want them to do." - [Sam Altman, CEO of OpenAI](. Large Language Models (LLMs) hold immense potential, but to fully unleash their capabilities, they require specific task-oriented training. Guiding and refining them is crucial to foster innovation. This week our focus is on utilizing these solutions to enhance workflow in data science and machine learning. To achieve our goals, it is crucial to seek guidance from skilled professionals who have already mastered the domain. Learning from their expertise can be pivotal in attaining success. This is why we are introducing a dedicated section from our upcoming book, “[Creators of Intelligence]( that highlights the real-world experiences of esteemed AI & Data Science experts. This Week’s Highlights: - [Build Your Own ChatGPT-Like App with Streamlit]( - [Building LLMs-Powered Apps with OPL Stack]( - [How To Deploy PyTorch Models as Production-Ready APIs]( - [Boost your forecast accuracy with time series clustering]( If you’re interested in sharing ideas to foster the growth of [_datapro]( then this survey is for you. Consider sharing your thoughts and get a FREE bestselling Packt book, The Applied Artificial Intelligence Workshop as PDF. Jump on in! [TELL US WHAT YOU THINK]( Cheers, Merlyn Shelley Editor in Chief, Packt Recent Forks on GitHub Large Language Models - [microsoft]( A library for helping developers craft prompts for Large Language Models. - [tatsu-lab]( Code and documentation to train Stanford's Alpaca models, and generate the data. - [openai]( This is an evaluation harness for the HumanEval problem-solving dataset described in the paper "[Evaluating Large Language Models Trained on Code](. - [project-baize]( Train your chatbot fast with ChatGPT's GPU power in just a few hours. - [nebuly-ai]( A library that allows you to create hyper-personalized ChatGPT-like assistants using your own data. - [OptimalScale]( Extensible Toolkit for Finetuning and Inference of Large Foundation Models. - [FMInference]( Running large language models on a single GPU for throughput-oriented scenarios. - [huggingface]( A text generation inference used in production at [HuggingFace]( to power LLMs API-inference widgets. - [EleutherAI]( implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library. [Pledge your support]( In the Industry AWS - [Boost your forecast accuracy with time series clustering:]( Time series are sequential data points over time analyzed for business decisions. AWS offers low/no code services for both ML and non-ML practitioners to build ML solutions. This post focuses on separating time series datasets into clusters for improved accuracy using local or global models. - [Recommend top trending items to your users using the new Amazon Personalize recipe:]( Amazon Personalize is a machine learning service that helps developers deliver personalized experiences. The new [aws-trending-now]( recipe identifies rapidly popular items and customizes recommendations to changing trends. This post shows how to use it for recommending top trending items to users. Google Cloud - [Unify your data assets with an open analytics lakehouse:]( Google Cloud offers an analytics lakehouse that combines data lakes and data warehouses without their overhead, supporting predictive analytics using BigQuery ML. It enables all users to become part of the data and AI ecosystem, with resilient automation and orchestration of repeatable tasks. With this feature, organizations can leverage data science capabilities at scale. - [Vertical autoscaling for batch Dataflow Prime jobs:]( Vertical autoscaling for batch Dataflow Prime automatically monitors memory usage and triggers memory upscaling after four out-of-memory (OOM) errors, preventing job failures and ensuring resilience without requiring manual intervention, thus allowing users to focus on application and business logic. Just for Laughs! Why did the language model start generating random emojis? It ran out of memory and had to use some simple graphics to convey its ideas! Streamline your Data Strategy with Experts [Creators of Intelligence]( – By [Dr. Alex Antic]( Are you keeping up with the latest developments in LLMs? Data science and AI are revolutionizing many industries at an unprecedented pace. Learning from industry experts is a wise way to stay updated. Their insights can help us improve our work, and decision-making, and uncover useful information. Our goal is to offer valuable solutions by leveraging the firsthand experience of industry leaders. That’s why we are introducing our latest content series featuring practical insights from 18 data and ML experts, that are extracted from our upcoming book “[Creators of Intelligence](. Here, we present a quick taste of what's inside the book, the author [Dr. Alex Antic]( engages in an interview-style Q&A session with AI leaders, delving into their areas of expertise, and crafting relevant questions to extract meaningful problem-solving strategies that you can apply at work. This curation of real-world experience is the perfect guide for anyone who wants to level up data practices and avoid common pitfalls. It's a great chance to learn from industry leaders! For this week, we'd like to share an intriguing response from Cortnie Abercombie, the CEO and Founder of [AI Truth]( and the former CDO of IBM, on How to Establish a Strong Data Culture. Alex Antic: In your opinion, what does an effective data culture look like? How do you advise organizations on building a data culture? Cortnie Abercombie: It goes back to the C-suite culture: the people in charge, how they view data, and how involved or how data-literate they are really affect the data culture. The reason that people have poor data culture is usually that they have people who don't know anything about data at the top. There can be problems both at the bottom and the top of the organization. I have a top 10 list on my website about this, in an article about when, as a data professional, you should [just walk away from a situation]( because you're not really going to get anywhere. There can be this over-amorous feeling about data. The CEOs and C-suite-level people can sometimes think it’s going to solve world hunger! They don't have a clue what it's actually supposed to be able to do within their bounds and their company, but they think that it does a lot more, and they think that somehow the data's going to do whatever analysis needs to be done itself. They don't think about the people who are actually performing the analysis, how long it takes people to get things done, and how much data needs to be cleaned up. [Find out more here!]( This exclusively curated content is extracted from the upcoming book “[Creators of Intelligence]( by [Dr. Alex Antic.]( To learn more about the in-depth concepts discussed here, check out the button below! [EXPLORE NEW IDEAS & READ ON!]( Find Out What’s New? - [Hands-on Generative AI with GANs using Python: DCGAN:]( The paper introduces DCGAN, a deep convolutional generative adversarial network for image synthesis in PyTorch. DCGAN utilizes convolution operations and two adversarial networks, a generator and a discriminator, to improve the quality of synthetic image generation. - [Build Your Own ChatGPT-Like App with Streamlit:]( This tutorial offers a comprehensive guide on building a custom chat interface for GPT-based models. The author utilizes Streamlit and Python to create a unique UI that offers greater customization and a more basic UX than existing apps like ChatGPT. The code is available on [GitHub]( and the tutorial provides detailed steps to guide the development process. - [Building LLMs-Powered Apps with OPL Stack:]( The post explores using LLMs with the OPL stack, comprising [OpenAI]( [Pinecone]( and [Langchain](. It covers overcoming LLMs limitations with domain knowledge, essential components with code walkthrough, production considerations, and common misconceptions, for building powerful applications. - [How To Deploy PyTorch Models as Production-Ready APIs:]( This article addresses challenges faced by ML engineers when deploying deep learning models in production, including rewriting boilerplate code and the need for a range of skills/tools. The solution combines [PyTorch Lightning]( and [BentoML]( to build a production-ready image classification service deployed to a Kubernetes-native environment. - [Data Pipeline Orchestration:]( Data pipeline management simplifies data deployment and increases availability for analytics. DataOps teams use orchestration to centralize administration and oversee pipelines. Infrastructure as code simplifies deployment eliminates errors and accelerates data engineering and MLOps tasks. Learning it requires time and effort, but it's a valuable skill. - [No More OOM-Exceptions During Hyperparameter Searches in TensorFlow:]( Faster hardware has proven to be a significant advantage in training larger machine learning models within a shorter timeframe. However, during hyperparameter optimization, Out of Memory (OOM) errors can pose a challenge. To mitigate this challenge, the use of the Python multiprocessing library has been identified as a viable solution. By leveraging this library and running each hyperparameter trial in its own process, the OOM errors can be avoided. See you next time! As a GDPR-compliant company, we want you to know why you’re getting this email. The _datapro team, as a part of Packt Publishing, believes that you have a legitimate interest in our newsletter and its products. Our research shows that you opted-in for email communication with Packt Publishing in the past and we think your previous interest warrants our appropriate communication. If you do not feel that you should have received this or are no longer interested in _datapro, you can opt out of our emails by clicking the link below.   [Like]( [Comment]( [Share](   Read Packt DataPro in the app Listen to posts, join subscriber chats, and never miss an update from Packt SecPro. [Get the iOS app]( the Android app]( © 2023 Copyright © 2022 Packt Publishing, All rights reserved. Our mailing address is:, Packt Publishing Livery Place, 35 Livery Street, Birmingham, West Midlands B3 2PB United Kingdom [Unsubscribe]() [Start writing]()