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Data Science Weekly Newsletter
October 1, 2020
The New Business of AI (and How It’s Different From Traditional Software)
We [Andreessen Horowitz] are huge believers in the power of AI to transform business: We’ve put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don’t have the same economic construction as software businesses. At times, they can even look more like traditional services companies...
Spotify Unwrapped: How we brought you a decade of data
The Spotify Wrapped Campaign is one of Spotify’s largest marketing and social campaigns of the year. It enables our users to see a detailed breakdown of their listening habits over the past year. Since 2019 was the end of the decade, we wanted to do something special for our users. As one of the few streaming platforms that has existed since before 2010, Spotify had a unique opportunity to be able to provide users with a review of their listening habits over the entire decade. This was an ambitious goal and one that posed many engineering challenges...
The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible...
The 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms is now available, and Domino is named a Visionary. Read the full analysis of Domino and other vendors in the report.
Data Science Articles & Videos
How Explainable AI Is Helping Algorithms Avoid Bias
Artificial intelligence is biased. Human beings are biased. In fact, everyone and everything that makes choices is biased, insofar as we lend greater weight to certain factors over others when choosing. Still, as much as AI has (deservedly) gained a reputation for being prejudiced against certain demographics (e.g. women and people of colour), companies involved in artificial intelligence are increasingly getting better at combating algorithmic bias...
Observability for Data Engineering
Until now, Observability has lived in the realm of DevOps or DevSecOps, focused on applications, microservices, network, and infrastructure health. But the teams responsible for managing data pipelines (Data Engineers and DataOps) have largely been forced to figure things out on their own. This might work for organizations that aren’t heavily invested in their data capabilities, but for companies with serious data infrastructure, the lack of specialized management tools leads to major inefficiencies and productivity gaps...
Love Me, Love Me Not: Classifying Text with Tensorflow and Twilio
Valentine's Day is coming up [editor: well, now past!] and both love and machine learning are in the air. Some would use flower petals to determine if someone loves them or not, but developers might use a tool like TensorFlow. This post will go over how to perform binary text classification with neural networks using Twilio and TensorFlow in Python...
Three Types of Data
In my work I've developed a mental framework related to data modeling, which has helped greatly both when coming up with a model and when making decisions down the road about how to use that model. Here I will establish three different categories of data in software: Constants, State, and Cached Values...
5 days, 8 tracks, 160 speakers and over 150 exciting sessions
Join Machine Learning Week 2020 , May 31 – June 4, Las Vegas! It brings together five co-located events: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, Deep Learning World. This event is where to meet the who’s who and keep up on the latest techniques, making it the leading machine learning event that excites and unites. You can expect top-class experts from world-famous companies such as Google, Microsoft, Lyft, Verizon, Visa and LinkedIn!
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To ensure continued success in the food and beverage space, PepsiCo has assembled a dedicated eCommerce team – tasked with optimizing eCommerce operations and developing innovations that will give PepsiCo a sustainable competitive advantage. While tied closely to broader PepsiCo, the eCommerce group more closely resembles a start-up environment; embracing the core values of having a bias for action, being results-oriented, maintaining a community-focus, and prioritizing people.
PepsiCo’s Data Science and Analytics group is a team of data scientists, technology specialists, and business innovators who operate within eCommerce to build industry-leading systems and solutions. By focusing on machine learning and automation, the Data Science & Analytics group is pushing the bounds of possibility for PepsiCo and its strategic partners.
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Training & Resources
Computer Vision Basics in Microsoft Excel
Computer Vision is often seen by software developers and others as a hard field to get into. In this article, we'll learn Computer Vision from basics using sample algorithms implemented within Microsoft Excel, using a series of one-liner Excel formulas. We'll use a surprise trick that helps us implement and visualize algorithms like Face Detection, Hough Transform, etc., within Excel, with no dependence on any script or a third-party plugin...
Trax — your path to advanced deep learning
Trax helps you understand and explore advanced deep learning. We focus on making Trax code clear while pushing advanced models like Reformer to their limits. Trax is actively used and maintained in the Google Brain team. Give it a try, talk to us or open an issue if needed...
Understanding the Neural Tangent Kernel
In this post, I’ll present a simple and intuitive introduction to this theory that leads to a proof of convergence of gradient descent to 0 training loss. I’ll make use of a simple 1-d example which lends itself to neat visualizations to help make the ideas easier to understand...
Data Science in Production: Building Scalable Model Pipelines with Python This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production....
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page . P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian