Remove 2017 Remove Data Science Remove Experimentation
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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. The approach they’ve used applies to other popular data science APIs such as NumPy , Tensorflow , and so on.

Metadata 105
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.

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Smarten Advanced Data Discovery is All the Buzz!

Smarten

Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist, ETL or developer. Advanced Data Discovery ensures data democratization by enabling users to drastically reduce the time and cost of analysis and experimentation.

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Is Google Cloud Platform Ready to Run Your Data Analytics Pipeline?

Sanjeev Mohan

I spent the majority of my time helping clients decide which was the right Hadoop platform and which NoSQL / nonrelational data store to pick for specific use cases. Fast forward to early 2017. Then in the middle of 2017, a realization set in that we were one year away from GDPR and needed to focus on data governance.

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Some highlights from 2020

Data Science and Beyond

I’ve been working remotely with Automattic since 2017, so I was pretty covid-ready as far as work was concerned. My main "day job" focus in 2020 was on being the tech lead for Automattic’s new experimentation platform (ExPlat). Remote work. Technical work.

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6 trends framing the state of AI and ML

O'Reilly on Data

Reinforcement learning fell by 5% in 2019; it’s up hugely—1,500+%—since 2017, however. Aggregating artificial intelligence and machine learning topics accounts for nearly 5% of all usage activity on the platform, a touch less than, and growing 50% faster than, the well-established “data science” topic (see Figure 2).