Data Elixir - Data Elixir - Issue 392
ISSUE 392 · June 21, 2022TrendsResearch highlights: Meta's Core Data Science teamMeta's research interests have a huge impact on the tools, applications, and methods that are used in the data science community. This post explores key areas of research in things like experimentation, privacy, economic modeling, statistics, and much more. This is well-organized and includes links to papers, posts and projects along the way. OrganizationsMore effective data teams using the JTBD frameworkAs a Data Strategist at Amplify Partners, Emilie Schario has worked with dozens of data teams and has seen a consistent pattern of JTBDs (Jobs-To-Be-Done) that data teams serve within organizations. This is a great post about the JTBD framework, how it can be used to keep your team focused, and the five specific JTBDs that data teams serve. A Better Way to Put Your Data to WorkThis article explores how successful companies are getting more value from their data by managing it like a product. Sponsored Link6 best practices to save time when creating AI data78% of machine learning projects stall before deployment, making speed a primary concern for AI teams. However, wading through unstructured data to accurately annotate assets requires a tremendous amount of patience, organization, and time. This webinar will cover six key time-saving practices implemented by leading AI teams when handling training data. Save your spot today. Tutorials, Projects & OpinionsA framework for document processing solutionsExtracting data from PDFs and scanned documents is more complicated than you might think. In this post, Lj Miranda explores the issues and walks through a framework for designing a document processing solution. Includes links to papers, recipes, and repos along the way. Graph Machine Learning at AirbnbReal-world machine learning problems can often be framed as graph problems. For instance, on online platforms, users may interact and share photos, messages, etc with each other and those connections form edges that can be used to create a graph. In this post, Devin Soni introduces graph neural networks with practical applications at Airbnb. Introduction to Graph Neural Nets with JAX/jraphTo go further with graph neural networks, here's a more in-depth introduction from DeepMind. This is easy to follow and is set up as a notebook so you can run the tutorial and interact with it. Mona: Intelligent Monitoring Solution for AI / MLTrusted by data science teams from top companies around the world, Mona provides you with complete visibility into the performance of your AI-driven products, automatically surfacing issues within your data and models. Track custom metrics for any AI use case, any industry and any tech stack. Try it for free or book a demo
today! ResourcesR Workflow for Reproducible ResearchGreat best-practices resource for reproducible research. Covers data documentation and manipulation, statistical analysis, graphics, reporting and more. It's written from the perspective of biomedical research but the methods here are widely applicable. Data VisualizationCreating flowcharts with {ggplot2}Flowcharts can be a useful way to visualise complex processes, especially when they can be created programmatically. This tutorial shows how to create highly customisable, good looking flowcharts using {igraph} and {ggplot2}. Was this email forwarded to you? Sign up here >> |
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Data Elixir - Issue 391
Tuesday, June 14, 2022
ML design patterns. Awesome data leadership. Faster Pandas. ML experimentation in VS Code.
Data Elixir - Issue 390
Tuesday, June 7, 2022
Friendlier SQL. Decision Intelligence framework. Collective data rights.
Data Elixir - Issue 389
Tuesday, May 31, 2022
The technical pay gap. DS: Foundations, Challenges, Opportunities. Existential threat of data quality. ML visual explainers.
Data Elixir - Issue 388
Tuesday, May 24, 2022
Software development for DS. How random forests really work. Visualizing multicollinearity. ML for conservation.
Data Elixir - Issue 387
Tuesday, May 17, 2022
Supervised clustering. Bandits for recommender systems. JavaScript for R. Teaching data science at scale.
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