20 Oct

ben weber zynga


One of the constraints that we had was that all of the inputs tables in our entity set need to be stored as a single table, and I’ll describe why we had this constraint later on. Last year we presented at Spark Summit on predicting retention for new installs and this year we showcased our automated modeling capabilities. San Francisco Bay Area. We have central and embedded analytics teams that use PySpark to support mobile publishing operations including analytics and reporting, experimentation, personalization services, and marketing optimization. We use Featuretools in AutoModel to perform deep feature synthesis.

If additional relationships are added to the entity set, even more features can be generated at different depths. Help us improve our Author Pages by updating your bibliography and submitting a new or current image and biography. This wasn’t a problem for us, since we separated feature engineering and feature application into separate pipeline phases, but it does mean that the schema of the Pandas UDF needs to be constant and predefined. Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. I recently self published a book on building data science workflows in Python. Machine learning has been transformational over the past decade, and Zynga has been exploring recent tools to automate much of our data science workflows. The goal of this post is to provide an overview of my session and details about our new machine learning capabilities. Our studios are located across the globe, and most teams have embedded analysts or data scientists to support the live operations of our games. The goal of the book is to provide readers with hands-on experience with cloud computing environments and large-scale machine learning pipelines. Our workaround for this issue was to cast all float data types to a type supported by Arrow. Copyright © 2020 Informa PLC He … One of the steps omitted in this block is the definition of the relationships in our entity set.

While XGBoost is not native to MLlib, we’ve integrated it into our pipeline in addition to gradient boosted trees, random forests, and logistic regression. When we first tried out Featuretools on a sample of our data, we were excited to see how well the models performed that we trained using the generated features.



This means that our predictive models need to scale beyond the single machine setups that we historically used when training models.
Once you have represented your data as entity sets, you can perform deep feature synthesis to transform the input data sets into an output data set with a single record per target entity, which is a customer for our example. The title of my session was “Automating Predictive Modeling at Zynga with PySpark and Pandas UDFs” and I was able to highlight our first portfolio-scale machine learning project. Sign up for your own profile on GitHub, the best place to host code, manage projects, and build software alongside 50 million developers. bgweber has no activity It generates a wide space of feature transformations and aggregations that a data scientist would explore when manually engineering features, but does so in a programmatic method. Ben Weber is a distinguished data scientist at Zynga with past experience at Twitch, Electronic Arts, Daybreak Games, and Microsoft Studios. 8. There are four key steps to using a Pandas UDF: The result of this code snippet is that the NHL data set can be distributed across a Spark cluster to perform the leastsq function on a large set of players. We needed a way to both parallelize and distribute the feature application process across a cluster of machines. If you enjoy reading this site, you might also want to check out these UBM Tech sites: What happened with Microsoft's Switch publishing experiments? whose registered office is 5 Howick Place, London, SW1P 1WG. For example, a match-3 game may record level attempts and resulting scores, while a casual card game may record hands played and their outcomes.

I’m a distinguished data scientist at Zynga and a member of the analytics team, which spans our central technology and central data organizations.

I presented at Spark Summit 2020 about how we opened, While Python and R provide rich ecosystems for data scientists to handle a wide range of problems, there are situations in which other…, Deploying Production-Grade Containers for Model Serving, In the past decade, self publishing books has became more common due to improved tools and platforms for authoring and selling books. 2, Java We would like to show you a description here but the site won’t allow us. One additional challenge we faced was that the schema for the returned Pandas dataframe needs to be specified before the function is defined, since the schema is specified as part of the grouped map annotation.

Ben Weber.

Prevent this user from interacting with your repositories and sending you notifications. ... A repository for Ben Weber's dissertation project Java 22 3 RServer. However, we found that using this approach was too slow when generating thousands of different features. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. However, the takeaway is to show how you can use libraries such as Featuretools, which require Pandas dataframes, and scale them to massive data sets. We use the outputs of AutoModel to personalize our games and live services. The output of this step is models that we use to predict player behavior in our games. Please try your request again later. The example below works with the Kaggle NHL (Hockey) data set, which describes a number of different games for each active player on an NHL roster since 2007. For more information, see our Privacy Statement. Over the past two years, analytics at Zynga has been increasingly using PySpark, which is the Python interface to the Spark big data platform. If you’re interested in machine learning at Zynga, we are hiring for data science and engineering roles. Luckily, our data is already in this format: each event has a player, category, and subcategory field, in additional to other columns that provide additional information. 4 Follow. But we had a problem, we needed to scale up from tens of thousands to tens of millions of users in order for this approach to work in production. There's a problem loading this menu right now. One of the common types of predictive models built by our data scientists are propensity models that predict which users are most likely to perform an action, such as making a purchase. Take a look at the
The figure below visualizes how Pandas UDFs enable large Spark dataframes to be decomposed into smaller objects, transformed with user code, and then recombined into a new Spark frame. San Francisco Bay Area. Registered in England and Wales. commits in Principal Data Scientist at Zynga. 22

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