Data Elixir - Data Elixir - Issue 386
ISSUE 386 · May 10, 2022OrganizationsHiring Data Scientists With IntentionHiring is hard. Especially, for data roles. This is an insightful post that offers a systematic and practical hiring approach that will help ensure you get the right people with the right skills on your team. Sponsored LinkIntroduction to Automated Data LabelingPreparing high quality vision datasets is a long and cumbersome process. This is why companies are now using automation to accelerate labeling and QA. Take a dive into automating data preparation with Superb AI and whether or not automation is right for you and your team. Tutorials, Projects & OpinionsPaper reading time - Forgetting in Data ScienceRandy Au dives into a paper from CHI 2022 where Michael Muller and Angelika Strohmayer explore the human work in data science and how, when, and where things are forgotten throughout the data pipeline. In Conversation with Barr Moses, CEO, Monte CarloHow do you know you can trust the data that you're using? In this interview, Barr Moses, CEO of the rapidly growing Monte Carlo, explores common issues that you're probably not thinking about. How to measure data qualityGreat starting point for thinking through why and how to measure data quality in your organization. Superb AI : Automate Data PreparationPreparing high quality vision datasets is a long and cumbersome process. This is why companies are now using automation to accelerate labeling and QA. Take a dive into automating data preparation with Superb AI and whether or not automation is right for you and your team. Machine LearningIndustrial Scale Text ClassificationWhat do you do when a client comes to you with a problem, unrealistic performance requirements, and your company’s survival depends on the solution? This is a great, first-hand account of building a prediction system in the real world. What about MLOps?Partly inspired by Laszlo Sragner's post, above, Shreya Shankar breaks down key considerations for your machine learning platform, depending on how far along you are. How Should you Protect your ML Models and IP?How do you make sure your models don't end up helping your competitors? The answer depends on your specific situation but after helping hundreds of teams ship ML-based products, Pete Warden has a few practical suggestions. Data VisualizationHorizon ChartsHorizon Charts are a specialized type of chart that allow you to display dense time series data in a limited amount of space. They're useful but can be tricky to understand. This is a slick, interactive explainer that makes it easy to see how Horizon Charts work. Visualization using R for researchers who don't use RThis hands-on tutorial is a great introduction to using R for data visualization. Following a "Getting Started" guide, sections cover data transformations, summary stats, advanced plots, styling, and more. Was this email forwarded to you? Sign up here >> |
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Data Elixir - Issue 385
Tuesday, May 3, 2022
Making data actionable. Using BIG AI models in a startup. From academia to industry. ML validity. Mental models for visualization.
Data Elixir - Issue 384
Tuesday, April 26, 2022
Data tests. Null Island. Confidence intervals for ML classifiers. Containers for ML. Performance utilities for regression modeling.
Data Elixir - Issue 383
Tuesday, April 19, 2022
Data teams: embedded or centralized? Unskilled and unaware of it. Counterfactual evaluation. Quant UX vs data science.
Data Elixir - Issue 382
Tuesday, April 12, 2022
Quarto. ML notebook tutorials. Reproducible & trustworthy workflows. Real-world recommenders. Graph-based outlier detection.
Data Elixir - Issue 381
Tuesday, April 5, 2022
Causal inference: core methods & tools. How to read papers. Data strategies with regular people. Preferences in recommenders.
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