





















































3 Days. 25+ AI Experts. 30+ Sessions.
Join the Generative AI In Action conference from Nov 11-13 (LIVE | Virtual) and gain insights from top AI leaders across over 30 sessions. Explore key topics including GenAI tools, AI Agents, Open-Source LLMs, Small Language Models, LLM fine-tuning, and many more! This is your opportunity to dive deep into cutting-edge AI strategies and technologies.
Save 40% with our Early Bird offer using code BIGSAVE40 – don’t miss out!
🦋 Welcome to BIPro #76 – Your Weekly Business Intelligence Power-Up! 🚀
Gear up for a fresh batch of BI trends, cutting-edge strategies, and top insights to supercharge your data journey!
📚 Must-Read BI Books of the Week
✦ The AI Value Playbook: How to Make AI Work in the Real World
✦Building LLM-Powered Apps: Level up with AI-driven applications.
✦Python for Algorithmic Trading Cookbook: Unleash the power of Python in trading.
✦Microsoft Power BI Cookbook (3rd Ed.): Master Power BI like a pro.
✦The Definitive Guide to Power Query (M): Dominate data wrangling in Power BI.
✦Mastering PyTorch (2nd Ed.): Deep dive into PyTorch for AI innovation.
🎯 Handpicked Articles Just for You!
✦Master Data Transformation with R & dplyr: Elevate your data manipulation game.
✦Eigenvalues & Eigenvectors in NumPy: Tackle advanced math with Python.
✦Import Data into BigQuery – Here’s How: Seamlessly load data like a pro.
✦Visualize with ggplot2 in R: Transform raw data into stunning visuals.
✦Query Microsoft Fabric GraphQL API: Easily integrate with external apps.
Stay ahead of the curve with these latest insights!
Calling All Data & BI Enthusiasts!
Do you dream of sharing your insights and building your reputation in the Data & BI community? Contribute to our new column in the Packt BIPro newsletter! Share your experiences, discuss new BI tools, or ask questions. Gain recognition among 37,000 BI professionals. Reply with your Google Docs article or use our weekly feedback form. Enjoy a free PDF of "Interactive Data Visualization with Python - Second Edition" for participating. Click reply or share your content today!
Share your thoughts and opinions here!
Cheers,
Merlyn Shelley
Editor-in-Chief, Packt
➽ Building LLM Powered Applications: This new titleis all about helping engineers and data pros use large language models (LLMs) effectively. It tackles key challenges like embedding LLMs into real-world apps and mastering prompt engineering techniques. You’ll learn to orchestrate LLMs with LangChain and explore various models, making it easier to create intelligent systems that can handle both structured and unstructured data. It’s a great way to boost your skills, whether you’re new to AI or already experienced! Start your free trial for access, renewing at $19.99/month.
➽ Microsoft Power BI Cookbook - Third Edition: The Power BI Cookbook is your essential guide to mastering data analysis and visualization with Power BI. It covers using Microsoft Data Fabric, managing Hybrid tables, and creating effective scorecards. Learn to transform complex data into clear visuals, implement robust models, and enhance reports with real-time data. This updated edition prepares you for future AI innovations, making it a must-have for beginners and seasoned users alike! Start your free trial for access, renewing at $19.99/month.
➽ The Definitive Guide to Power Query (M): The Definitive Guide to Power Query (M) focuses on mastering data transformation with Power Query. It covers fundamental and advanced concepts through hands-on examples that address real-world problems. You'll learn the Power Query M language, optimize performance, handle errors, and implement efficient data processes. By the end, you'll have the skills to enhance your data analysis effectively! Start your free trial for access, renewing at $19.99/month.
Introducing The AI Value Playbook: How to Make AI Work in the Real World
By Lisa Weaver-Lambert, Data and AI Leader in Capital Markets, formerly Microsoft, and Accenture
Are you a business leader or board member intrigued by the groundbreaking advances in Generative AI (GenAI) and Large Language Models (LLMs)?
If you want to quickly formulate a perspective on how to integrate AI, The AI Value Playbook by Lisa Weaver-Lambert, is a must read. This book addresses the gap in data and AI knowledge in leadership teams that have an appetite for nuanced, targeted and practical solutions. It includes which levers and processes to consider to future-proof businesses. The AI Value Playbook draws on conversations and case studies with leading practitioners across sectors and geographies who share their first-hand experiences successfully driving AI value and pathways for progress.
Why is This Book a Must-Read for Business Leaders?
Business leaders are challenged by the speed of AI innovation and how to navigate disruption and uncertainty. This book is a crucial resource for those who want to understand how to leverage AI to drive business value, drawn from the firsthand experience of those who have been implementing this technology successfully.
In a series of over 30 in-depth and wide-ranging conversations with practitioners, from CEOs leading new generative AI-based companies to Data Scientists and CFOs working in more traditional companies share their hard-earned wisdom. They talk candidly about their successes and failures, and what excites them about the future. These interviews offer unique insights for business leaders to apply to their own organizations. The book distils a value-driven playbook for how AI can be put to work today.
Experts include:
Sam Liang, CEO of Otter.ai
Amr Awadallah, Founder and CEO at Vectara
Philipp Heltewig, Co-Founder and CEO at Cognigy
Joshua Rubin, Principle AI Scientist at Fiddler AI
Zeev Farbman, Co-Founder & CEO at Lightricks
…and many more innovators who are actively shaping the AI landscape.
Key Topics Covered in the Playbook
This book provides case studies which explore the specifics of real-world applications. These present detailed analyses of practical scenarios, offering a closer look at the application and impact of AI, such as:
How Generative AI Transforms Healthcare Education (LLMs & RAG enabling hyper-personalized learning for healthcare technicians)
AI-Powered Virtual Agents Improving Service Efficiency (Real-world examples of AI's impact on customer service operations)
Unlocking Profit with AI (Leveraging enterprise data for increased customer profitability and minimizing churn)
The Role of Multimodal LLMs in Software Development (Innovations that redefine customer interaction and product creation)
The last section of the book is The ‘AI Value Playbook’ a practical framework distilled from the experts and Lisa’s own professional experience, for successful AI implementation.
Answers to the Big Questions for Business Leaders
The book tackles the pressing questions business leaders are facing today, such as:
How can organizations adapt to the rapid pace of AI innovation?
How do we strategically deploy AI to enhance efficiency and drive business value?
What risks and ethical considerations should be addressed?
How quickly can we start seeing measurable benefits from AI integration?
What You’ll Take Away
The AI Value Playbook distils a value-driven playbook for how AI can be put to work today, including:
Fundamentals of AI concepts and the tech stack
How AI works with real-world practical applications
How to integrate into your company’s overall strategy
How to incorporate generative AI in your processes
How to drive value with sector-wide examples
How to organize an AI-driven operating model
How to use AI for competitive advantage
The dos and don’ts of AI application
With endorsements from Said Business School, University of Oxford, Microsoft leaders, Private Equity and Venture Capital leaders and board leaders, don't miss out on this opportunity to learn from the practical scenarios and strategic plays. The AI Value Playbook is a versatile resource and roadmap to making AI work in the real world—starting today.
➽ How to Use R for Data Transformation with dplyr? This blog explores overcoming challenges in fine-tuning and deploying large language models (LLMs) using R's 'dplyr' package. It covers installation, selecting and renaming columns, and filtering rows to streamline data transformation for effective analysis in R.
➽ How to Calculate Eigenvalues and Eigenvectors with NumPy? This blog explains how to calculate eigenvalues and eigenvectors using NumPy's linear algebra module. It covers the mathematical background, provides practical coding examples, and discusses the implications of eigenvalues in applications like Principal Component Analysis (PCA) for dimensionality reduction.
➽ How to Import Data into BigQuery? This tutorial demonstrates how to load datasets into Google BigQuery from various sources, including CSV, JSON, Google Cloud Storage, and Google Sheets. It outlines prerequisites, interface navigation, and step-by-step procedures for each data loading method, focusing on Asian cuisine examples.
➽ How to Visualize Data with ggplot2 in R? This article introduces ggplot2, an R package for creating visualizations like scatter plots, line plots, bar plots, and more. It covers installation, basic and advanced plot types, and how to save plots, helping users effectively visualize data.
➽ How to Evaluate RAG If You Don’t Have Ground Truth Data? This article discusses strategies for evaluating Retrieval-Augmented Generation (RAG) models without ground truth data. It covers retrieval and generation evaluations, including using vector similarity thresholds, multiple LLMs for response comparison, and human feedback to establish criteria for relevance, correctness, and fluency. Additionally, it outlines methods to create a ground truth dataset from scratch using existing datasets or manual curation.
➽ Start Asking Data Why? This blog explores how to uncover causal relationships in observational data without relying on expensive randomized control trials. It emphasizes the importance of understanding the story behind the data, introduces causal reasoning through Simpson’s and Berkson’s Paradoxes, and advocates for using causal graphs to enhance data analysis.
➽ Choosing Between LLM Agent Frameworks: This blog discusses the evolving landscape of AI agents, highlighting the shift from Retrieval-Augmented Generation (RAG) to modern frameworks for developing autonomous systems. It reviews various agent frameworks, compares their strengths and weaknesses, and provides insights on building agents from scratch, including challenges and benefits.
➽ Your Documents Are Trying to Tell You What’s Relevant: Better RAG Using Links. This article addresses the challenges in building retrieval-augmented generation (RAG) applications, particularly in document retrieval. It introduces a new data model—linked documents—that enhances performance by preserving references like citations and hyperlinks, improving the ability to find relevant information. The author discusses the limitations of vector searches and emphasizes the importance of document structure and connections. By implementing document linking, the article illustrates how to effectively retrieve and utilize related documents, ultimately enriching the response quality of RAG systems.
➽ Query Microsoft Fabric GraphQL API from an External App: This guide outlines how to query data from a Microsoft Fabric workspace using a C# application via the Microsoft Fabric GraphQL API. It details prerequisites, including creating a Microsoft Entra app and configuring necessary permissions for data access.
➽ Building an Interactive UI for Llamaindex Workflows: This article expands on using LlamaIndex workflows to enhance research and presentations by integrating a Streamlit UI. It outlines how to create a user-friendly interface that displays progress, collects user input, and generates downloadable slide decks while detailing backend enhancements and streaming event implementations.
➽ Achieve near real-time analytics on Amazon DynamoDB with SingleStore: This article outlines methods for integrating Amazon DynamoDB with SingleStore for near real-time analytics. It describes two architectural patterns: using DynamoDB Streams with AWS Lambda and leveraging Amazon Kinesis Data Streams with Amazon MSK, enabling efficient data capture and analysis.
➽ How AI Platforms Are Transforming Business Data Management? The article discusses the rapid growth of the AI market, valued at $196.63 billion in 2023, projected to grow at a CAGR of 36.6% through 2030. It highlights challenges in traditional data management, with the global data management market valued at $89.34 billion in 2022. The rise of platforms like Databricks and Snowflake is emphasized, with Databricks valued at $43 billion in 2023, contributing to an anticipated data analytics market value of about $550 billion by 2028. The piece emphasizes the importance of data quality and integration of open-source and closed-source models, advocating for industry-specific AI solutions for better performance.
➽ Introducing the OpenAI Academy: OpenAI is launching the OpenAI Academy to invest in developers and organizations in low- and middle-income countries, providing training, technical guidance, and $1 million in API credits. The initiative aims to enhance local AI talent, drive economic growth, and foster community innovation.
➽ Looker Chart Config Editor tips: Google Cloud is enhancing Looker’s capabilities with new visualization options, including bullet charts and sunbursts, accessible through the Chart Config Editor. This post shares tips on using the Highcharts API to improve data visualizations, emphasizing customization and scrolling features for better user experience.
➽ BigQuery jobs explorer is now GA: Google Cloud has launched BigQuery Jobs Explorer, a tool that provides comprehensive visibility into SQL query activity within organizations. It enables real-time monitoring, troubleshooting, and performance optimization, allowing users to track resource usage, identify costly queries, and improve overall efficiency.
➽ Create security observability using generative AI with Security Lake and Amazon Q in QuickSight: This blog discusses a serverless solution for querying Amazon Security Lake data using natural language through Amazon QuickSight’s Amazon Q. It highlights the benefits of integrating generative AI for security use cases, improving threat response and data analysis by leveraging CloudTrail logs, VPC Flow Logs, and AWS services.