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Data Science Weekly Newsletter
July 8, 2021

Editor's Picks

  • Pinterest Visual Signals Infrastructure:
    Evolution from Lambda to Kappa Architecture

    With the growing need for machine learning signals from Pinterest’s huge visual dataset, we decided to take a closer look at our infrastructure that produces and serves these signals. A few parameters we were particularly interested in were signal availability, infra complexity and cost optimization, tech integration, developer velocity, and monitoring. In this post, we will describe our journey from a Lambda architecture to the new real-time signals infrastructure inspired by Kappa architecture...
  • Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression
    When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible for storing and serving feature data. The decisions made here can prevent overrunning cost budgets, compromising runtime performance during model inference, and curbing model deployment velocity...

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Data Science Articles & Videos

  • A Review of Coffee Data: Grades and Flavors
    After trying a variety of coffees from around the world, I have often wondered how flavor differences affect grading (Q-grades or Cupping Grades). Even though I have found a general correlation between coffee grade and taste, I have really enjoyed even lower graded coffees. I have looked at two databases with coffee grades, and there are definitely regional differences, but I still didn’t have an idea how more specific flavors played a role...
  • Using Reddit to explore the mental health effects of the COVID-19 era.
    Preliminary research by the CDC has indicated that the COVID-19 pandemic has affected our health, both physically and mentally. Anxiety, depression, suicidal thoughts, and substance abuse disorders appear to be on the increase. I thought it might be interesting to look at subreddits specific to mental health issues and see what topics people are discussing the most and to see if there is increased engagement with these communities now that more people are distanced from physical sources of support and information...
  • Calibrating Deep Neural Networks using Focal Loss
    Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to be accurate, calibrated and confident. We show that, as opposed to the standard cross-entropy loss, focal loss [Lin et. al., 2017] allows us to learn models that are already very well calibrated...
  • create-ml-app
    A few months ago, I started using Makefiles for my local Python ML projects. Ever since, I haven’t manually dealt with venv or pip installs. It’s not life-changing, but I now can’t imagine starting a local ML project without a Makefile. Here’s a template...
  • Approximate Nearest Neighbor Search in Vespa — Part 1
    This blog post is part 1 in a series of blog posts where we share how the Vespa team implemented an approximate nearest neighbor (ANN) search algorithm. In this first post, we’ll explain why we selected HNSW (Hierarchical Navigable Small World Graphs) as the baseline algorithm and how we extended it to meet the requirements for integration in Vespa...
  • 2020’s Top AI & Machine Learning Research Papers
    Despite the challenges of 2020, the AI research community produced a number of meaningful technical breakthroughs. For example, teams from Google introduced a revolutionary chatbot, Meena, and EfficientDet object detectors in image recognition. Researchers from Yale introduced a novel AdaBelief optimizer that combines many benefits of existing optimization methods. OpenAI researchers demonstrated how deep reinforcement learning techniques can achieve superhuman performance in Dota 2. To help you catch up on essential reading, we’ve summarized 10 important machine learning research papers from 2020...
  • Introducing the NeurIPS 2020 Main Program
    We have just released the full schedule, two weeks ahead of the beginning of the conference, to let people familiarize themselves with it and plan their attendance accordingly, since it is hard to both attend the conference and maintain our busy daily schedule otherwise...
  • Differentially Private Learning Needs Better Features (or Much More Data)
    We demonstrate that differentially private machine learning has not yet reached its "AlexNet moment" on many canonical vision tasks: linear models trained on handcrafted features significantly outperform end-to-end deep neural networks for moderate privacy budgets. To exceed the performance of handcrafted features, we show that private learning requires either much more private data, or access to features learned on public data from a similar domain...


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  • Data Scientist - Apple Pay Analytics - NYC

    You will play a key role improving the Apple Pay product experience. As a member of the analytics team you will be supporting a product function. You will partner with business owners, understand goals, craft KPIs and measure ongoing performance. You will initially engage with the product and engineering teams in ensuring that we have the appropriate instrumentation in place to deliver on these metrics. You will subsequently use advanced statistical, ML and analytical techniques to analyze product performance and identify key insights that inform product improvements and business strategy. The role requires a high degree of independence, ownership and collaboration working cross functionally across all levels of a highly matrixed organization...
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Training & Resources

  • Rich
    Rich is a Python library for rich text and beautiful formatting in the terminal. The Rich API makes it easy to add color and style to terminal output. Rich can also render pretty tables, progress bars, markdown, syntax highlighted source code, tracebacks, and more — out of the box....
  • Reproducible and upgradable Conda environments:
    Dependency management with conda-lock

    If your application uses Conda to manage dependencies, you face a dilemma. On the one hand, you want to pin all your dependencies to specific versions, so you get reproducible builds. On the other hand, once you’ve pinned everything, upgrades become difficult: you’ll start encountering the infamous The following specifications were found to be incompatible with each other error. Ideally you’d be able to both have a consistent, reproducible build, and still be able to quickly change your dependencies. And you can do this—with a little understanding, and a bit more work...


  • Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
    Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems...
    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

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