Ratio of Data Scientists to Data Engineers
A fun topic of discussion among leaders of data teams is the ratio between the number of data scientists and data engineers. There is no ideal answer. It really depends on the tools and infrastructure you have in place, the maturity and availability of use cases for data and AI, and how you exactly define specific roles and titles. Usage of the title “data scientist” varies widely. I examine how some large data teams approach this subject:
Data Exchange podcast
- Building Machine Learning Infrastructure at Netflix and beyond: The team behind the popular Netflix open source project Metaflow has launched a new company.
- Imperceptible NLP Attacks: Nicholas Boucher is the lead author of a fascinating new paper that explores attacks against text-based NLP models, based on Unicode and other encoding systems.
- Evolving Data Science Training Programs: Anjali Samani is Director of Data Science and Data Intelligence at Salesforce. She and I first met at Faculty, where she helped design and lead the early Fellowship programs (intensive bootcamps that turn STEM graduates and postdocs into industrial data scientists).
Data & Machine Learning Tools and Infrastructure
- Reinforcement Learning in production: This virtual event showcases practical applications of reinforcement learning, a set of tools and techniques that many developers and machine learning engineers I have spoken with are eager to try. General admission is FREE and the tutorial passes are $75 → use the discount code GF50 if you plan to attend the afternoon tutorial.
- Differential Privacy for Python developers: Now more people will be able to process data with differential privacy – Google has made its DP tools available to Python developers!
- Orbit version 1.1 is out: Orbit is Uber’s open source Python library for Bayesian time-series analysis and forecasting. It’s based on popular probabilistic programming languages (PPL) including Stan and Pyro.
- A path forward for Interpretable Machine Learning
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