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# System prepended metadata

title: Wednesday Sessions-Aakash

---

Wednesday Sessions-Aakash
===
* 11:20am -12:00pm
    - We run, we improve, we scale: The XGBoost story at Uber 
11:20am-12:00pm, Sep 25 / 1A 08/10
    - Scaling data engineers
11:20am-12:00pm, Sep 25 / 1A 21/22
    - Building a multitenant data processing and model inferencing platform with Kafka Streams
11:20am-12:00pm, Sep 25 / 1A 15/16

* 1:15pm - 1:55pm
    - A productive data science platform: Beyond a hosted-notebooks solution at LinkedIn
1:15pm-1:55pm, Sep 25 / 1A 21/22
    - Machine learning and large-scale data analysis on a centralized platform
1:15pm-1:55pm, Sep 25 / 1A 08/10
    - Turning petabytes of data from millions of vehicles into open data with Geotab
1:15pm-1:55pm, Sep 25 / 1E 12/13

* 2:05pm - 2:45pm
    - Mastercard and Pitney Bowes: Creating a data-driven business (sponsored by Pitney Bowes)
2:05pm-2:45pm, Sep 25 / 1E 06
    - The evolution of metadata: LinkedIn’s story
2:05pm-2:45pm, Sep 25 / 1A 23/24
    - From raw data to informed intelligence: Democratizing data science and ML at Uber
2:05pm-2:45pm, Sep 25 / 1A 21/22

* 2:55pm - 3:35pm
    - Turning big data into knowledge: Managing metadata and data relationships at Uber's scale
2:55pm-3:35pm, Sep 25 / 1A 23/24
    - How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE (BlueData))
2:55pm-3:35pm, Sep 25 / 1E 17
    - Time travel for data pipelines: Solving the mystery of what changed
2:55pm-3:35pm, Sep 25 / 1E 07/08

* 4:35pm-5:15pm
    - Trill: The crown jewel of Microsoft’s streaming pipeline explained
4:35pm-5:15pm, Sep 25 / 1A 15/16
    - Solve tomorrow’s business challenges with a modern data warehouse (sponsored by Matillion)
4:35pm-5:15pm, Sep 25 / 1E 17
    - Predicting Criteo’s internet traffic load using Bayesian structural time series models
4:35pm-5:15pm, Sep 25 / 1A 12/14

* 5:25pm-6:05pm
    - Fast data with the KISSS stack
5:25pm-6:05pm, Sep 25 / 1A 15/16
    - How Brazil deployed a 160 million-person biometric identification system: Challenges, benefits, and lessons learned
5:25pm-6:05pm, Sep 25 / 1E 12/13
    - Causal inference 101: Answering the crucial "why" in your analysis
5:25pm-6:05pm, Sep 25 / 1A 12/14
