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bweber

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bgweber.medium.com 1y ago

Deep Learning in AdTech, a hands-on example with Kaggle

bweber
2pts0
www.gamasutra.com 4y ago

A Technical Introduction to Programmatic User Acquisition

bweber
2pts0
bgweber.medium.com 4y ago

Real-Time ML Approaches Explored at Zynga

bweber
1pts0
www.kdnuggets.com 5y ago

New Tools I Learned as a Data Scientist in 2020

bweber
4pts0
bgweber.medium.com 5y ago

New Tools I Learned as a Data Scientist in 2020

bweber
3pts0
www.gamasutra.com 5y ago

The Zynga Analytics Platform

bweber
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gamasutra.com 5y ago

The Zynga Analytics Platform in 2020

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1pts0
www.gamasutra.com 5y ago

Evolution of the Zynga Analytics data platform

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2pts0
towardsdatascience.com 6y ago

Securing ML Services on the Web

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2pts0
medium.com 6y ago

HTTPS and Access Control for Data Science Projects

bweber
2pts0
towardsdatascience.com 6y ago

DevOps for Data Science with GKE and Stackdriver

bweber
1pts0
medium.com 6y ago

DevOps for Data Science with GCP: Deploying Production-Grade Containers

bweber
4pts0
levelup.gitconnected.com 6y ago

Self Publishing a Technical Book in 2020

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9pts0
medium.com 6y ago

Self Publishing a Technical Book in 2020

bweber
2pts0
www.amazon.com 6y ago

Free Kindle Book on Python: “Data Science in Production”

bweber
2pts0
news.ycombinator.com 6y ago

Show HN: Self-Published Book – “Data Science in Production”

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172pts18
news.ycombinator.com 6y ago

Show HN: I Self Published a Book on “Data Science in Production”

bweber
1pts0
towardsdatascience.com 6y ago

Real-Time Scikit-Learn Models with Structured Streaming in PySpark

bweber
3pts0
medium.com 6y ago

Streaming Scikit-Learn with PySpark

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9pts0
levelup.gitconnected.com 6y ago

Scaling Scikit-Learn with Apache Beam and Cloud Dataflow

bweber
3pts0
medium.com 6y ago

Scaling Scikit-Learn with Apache Beam

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2pts0
towardsdatascience.com 6y ago

PySpark for Data Science Workflows (Book Chapter)

bweber
2pts0
medium.com 6y ago

PySpark for Data Science Workflows

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3pts0
www.gamasutra.com 6y ago

Automating Machine Learning for Mobile Games

bweber
2pts0
towardsdatascience.com 6y ago

Workflow Tools for ML Pipelines

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5pts0
towardsdatascience.com 6y ago

Automating ML Feature Engineering (AI Expo 2019 Talk)

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1pts0
medium.com 6y ago

Automating ML Feature Engineering

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5pts0
towardsdatascience.com 6y ago

Building an Applied Science Portfolio (ODSC 2019)

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1pts0
towardsdatascience.com 6y ago

Serverless Functions for Data Science

bweber
2pts0
medium.com 6y ago

An Introduction to Serverless Functions for Data Science

bweber
1pts0

This article doesn't even mention one of my favorite tools on GCP which is Dataflow. It's really easy to build batch and streaming workflows that can be used for data and machine learning pipelines. It really shows how well the different services within GCP are integrated, since it's trivial to set up PubSub or BigQuery as sources and sinks.

For paper size, I decided to use 6x9 from the start. I also didn't consider the epub format until the end, and then used the "print replica" feature on Kindle Direct to create a kindle version, which lacks text resizing and a few other features. Once I settled on a page size, I wrote each chapter independently and made sure to avoid any widowed text or code samples. I decided that orphaned text would be fine, given that the size of the page is relatively small.

I didn't really need to write any scripts, beyond using the sample bookdown project. I did use a custom book class and made some tweaks for the code formatting, but these were mostly Latex changes.

Virtual environments are useful when setting up a single machine, but many of the tools covered in the book do not directly support venv, such as Lambda functions, Cloud Dataflow, and Databricks. In general, the goal is to get readers to explore tools beyond Conda for setting up environments and dependencies.

Marketing will be a challenge. There's been great reception here, but I expect sales to taper quickly and then paid sponsorship will be necessary to continue generating sales. I'm currently testing out Amazon Advertising, but I don't seem to have bids high enough to get to my target budget.

Thanks for the feedback. As someone just getting started in this area, I was having trouble finding clear examples. Using the R interface probably isn't the best way to get started with custom loss functions, because the error messages you get from nested python calls are usually not too descriptive.

There are different tournaments for participants that want to focus on only a subset of the game. For example, you could hard code all locations for the tournament 3 contest and focus on macro.

While you can't run the Broodwar API on battle.net, it is possible to run bots on ICCup. But dont expect anybody to join the game, since you will not be running the antihack.