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On this week’s podcast, Danny Yuan, Uber’s Real-time Streaming/Forecasting Lead, lays out a thorough recipe book for building a real-time streaming platform with a major focus on forecasting. In this podcast, Danny discusses everything from the scale Uber operates at to what the major steps for training/deploy models in an iterative (almost Darwinistic) fashion and wraps with his advice for software engineers who want to begin applying machine learning into their day-to-day job.  

Why listen to this podcast:

* Uber processes 850,000 - 1.3 million messages per second in their streaming platform with about 12 TB of growth per day. The system’s queries scan 100 million to 4 billion documents per second.
* Uber’s frontend is mobile. The frontend talks to an API layer. All services generate events that are shuffled into Kafka. The real-time forecasting pipeline taps into Kafka to processes events and stores the data into Elasticsearch. * There is a federated query layer in front of Elasticsearch to provide OLAP query capabilities.
* Apache Flink’s advanced windowing features, programming model, and checkpointing convinced Uber to move away from the simplicity of Apache Samza.
* The forecasting system allows Uber to remove the notion of delay by using recent signals plus historical data to project what is happening now and what will happen into the future.
* Uber’s pipeline for deploying ML models: HDFS, feature engineering, organizing into data structures (similar to data frames), deploy mostly offline training models, train models, & store into a container-based model manager. 
* A model serving layer is used to pick which model to use, forecasting results are stored in an OLAP data store, a validation layer compares real results against forecast results to verify the model is working as desired, and a rollback feature enables poor performing models to be automatically replaced by previous one.
* “Without output, you don’t have input.” If you want to start leveraging machine learning, developers just need to start doing. Start with intuition and practice. Over time ask questions and learn what you need, then apply a laser focus to gain that knowledge.

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Show Notes

Streaming: Danny Yuan on Real-Time, Time Series Forecasting @Uber

Are you willing to make the digital shift to be a model-driven business?

If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/212

The Super Data Science Podcast: Big Data | Analytics Careers | Mentors | Success

SDS 212: Model Driven Vs Data Driven

When data scientists use a linear regression to look for causal relationships between a treatment and an outcome, what they’re usually finding is the so-called average treatment effect. In other words, on average, here’s what the treatment does in terms of making a certain outcome more or less likely to happen. But there’s more to life than averages: sometimes the relationship works one way in some cases, and another way in other cases, such that the average isn’t giving you the whole story. In that case, you want to start thinking about heterogeneous treatment effects, and this is the podcast episode for you.

Relevant links:
https://eng.uber.com/analyzing-experiment-outcomes/
https://multithreaded.stitchfix.com/blog/2018/11/08/bandits/
https://www.locallyoptimistic.com/post/against-ab-tests/

Heterogeneous Treatment Effects

Why do we pronounce data with a capital D?

Understanding Data
by Cassie Kozyrkov.

Read by the author.

Text version: bit.ly/quaesita_hist
Blog: medium.com/@kozyrkov
Videos: bit.ly/statthinking
Music by my friends Raised By Dinosaurs. Full song: bit.ly/dipod_song

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