Data Science Insider: October 20th, 2023

In This Week’s SuperDataScience Newsletter: Nvidia & Foxconn Unite for AI Factories. Hands-On Data Engineering with DataBricks. Efficient Object Detection Model Optimization. Microsoft Fabric Enhances Data Science in BI. AI Firm Anthropic Sued for Copyright Infringement by UMG. Cheers, - The SuperDataScience Team P.S. Have friends and colleagues who could benefit from these weekly updates? Send them to [this link]( to subscribe to the Data Science Insider. --------------------------------------------------------------- [Nvidia & Foxconn Unite for AI Factories]( brief: Nvidia, the world's leading chip company, and Foxconn, the manufacturer of iPhones, are collaborating to establish "AI factories." These cutting-edge data centres will leverage Nvidia's chips to power a diverse array of applications, including training autonomous vehicles, robotics, and large language models. This initiative is significant given recent US export restrictions on high-end AI chips destined for the Chinese market, impacting Nvidia. During Foxconn's annual tech showcase in Taipei, Nvidia's CEO Jensen Huang and Foxconn's Chairman Young Liu emphasized the emergence of a new manufacturing paradigm - one focused on intelligence production through AI factories. This development highlights the pivotal role of AI in manufacturing and the crucial need for data scientists to understand these advancements, as they drive growth. Why this is important: This partnership signifies the growing importance of AI in various industries, particularly manufacturing. AI factories have the potential to revolutionize production processes, leading to more efficient and intelligent systems. Data scientists should closely follow these developments as they shape the future of AI applications in manufacturing, opening up new avenues for data analysis, ML, and automation, thereby enhancing their role and expertise in this evolving landscape. [Click here to learn more!]( [Hands-On Data Engineering with DataBricks]( brief: Patrick Nguyen's Medium article offers a hands-on experience, allowing beginners to actively code data ingestion and transformation using DataBricks in under 30 minutes. DataBricks, enables massive data processing and transformation, though its many tools and concepts can be overwhelming for newcomers. To begin, you can sign up for DataBricks Community Edition for free, create a notebook, and attach clusters for parallel processing. The article walks you through using the New York Taxi Trip dataset and downloading it in Parquet format to DataBricks. You then read, process, and save the data, creating a view for SQL queries. The article concludes by showing how to use SQL and SparkSQL for data visualization. . Why this is important: In the realm of data science, understanding practical applications of buzzwords like Big Data, Data Engineering, Python, Spark, and DataBricks is essential. Data scientists, like you, should know these steps because they provide a practical introduction to using DataBricks for data engineering tasks, including data ingestion, transformation, and visualization. [Click here to read on!]( [Efficient Object Detection Model Optimization]( In brief: Data scientists need to adapt object detection models to their specific requirements efficiently. Object detection models, while effective, are often over-engineered for specific tasks. This Towards Data Science article discusses optimizing object detection models for more straightforward applications. It suggests changes to hyperparameters, such as reducing the number of predictions per class and bounding boxes, for more efficient processing. The article goes on to examine various object detection architectures and presents adjustments tailored to specific needs. For example, in simpler scenarios, the article recommends decreasing the number of layers and feature maps to enhance performance while conserving accuracy. Practical results demonstrate substantial improvements in inference time without compromising mean Average Precision. Why this is important: This article provides valuable insights into optimizing these models, allowing data scientists to save computational resources and enhance performance tailored to their unique use cases. Understanding how to fine-tune models is essential for efficient, real-world applications of object detection in data science. [Click here to discover more!]( [Microsoft Fabric Enhances Data Science in BI]( www.infoworld.com/article/3708988/bi-meets-data-science-in-microsoft-fabric.html In brief: In Microsoft's Fabric, a cloud-hosted data lake and lakehouse platform, data science tools are being integrated, and Power BI datasets are opened up to Python, R, and SparkSQL. A common data layer, or "data fabric," serves as a baseline of truth, enabling both short-term and long-term decision-making through real-time and historical data analysis. This common data layer encompasses both common programming languages and specialized analytics tools, bridging the gap between data engineering and data science. For data scientists, like you, the integration of Power BI data sets with Python and familiar tools like Pandas and Apache Spark APIs is important as it streamlines collaboration with BI teams, ensuring consistent data and model usage. Why this is important: Data scientists should understand this development as it simplifies the interaction between data science and business intelligence teams. The integration of Power BI datasets with Python and Pandas enables collaborative work and ensures consistent data and model usage. [Click here to see the full picture!]( [AI Firm Anthropic Sued for Copyright Infringement by UMG]( In brief: AI company Anthropic recently secured up to $4 billion in investment from Amazon, however, it now faces a lawsuit for copyright infringement filed by Universal Music Group (UMG). UMG has been fostering partnerships with companies in the generative AI and music space, but Anthropic seems to have crossed a legal line. The lawsuit, led by UMG's publishing company and co-plaintiffs, claims that Anthropic's product, Claude, unlawfully copies and disseminates copyrighted song lyrics to train its AI models, generating output that violates copyright laws. This legal action may set a significant precedent for the relationships between music rightsholders and tech firms. Data scientists must recognize the legal implications of AI content generation and copyright to ensure ethical and responsible AI usage. Why this is important: Data scientists need to be aware of the legal ramifications associated with AI-generated content and copyright infringement. As AI technology advances, ensuring ethical and responsible usage is crucial to avoid costly legal disputes and maintain positive relationships with rightsholders. [Click here to find out more!]( [Super Data Science podcast]( In this week's [Super Data Science Podcast]( episode, Jon Krohn speaks to Jerry Yurchisin, Data Science Strategist at Gurobi, the decision-making technology and best-kept secret of 80% of America’s leading enterprises. He explains mathematical optimization with case examples that highlight its differences from statistical or machine learning approaches. Jerry also gives his top recommendations for anyone who wants to get started with mathematical optimization, whatever your preferred programming language may be. [Click here to find out more!]( --------------------------------------------------------------- What is the Data Science Insider? This email is a briefing of the week's most disruptive, interesting, and useful resources curated by the SuperDataScience team for Data Scientists who want to take their careers to the next level. Want to take your data science skills to the next level? Check out the [SuperDataScience platform]( and sign up for membership today! Know someone who would benefit from getting The Data Science Insider? Send them [this link to sign up.]( # # If you wish to stop receiving our emails or change your subscription options, please [Manage Your Subscription]( SuperDataScience Pty Ltd (ABN 91 617 928 131), 15 Macleay Crescent, Pacific Paradise, QLD 4564, Australia