PyData Amsterdam - Schiphol Group

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Schiphol Real Estate

Evert van de Beekstraat 202 · Schiphol

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>>>> LOCATION OF THE MEETUP (otherwise you'll end up at the wrong place) >>>>

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>>>> LOCATION OF THE MEETUP (otherwise you'll end up at the wrong place)

Hi PyData folks,

Time for a post conference meetup! This time hosted by our sponsor Schiphol Group at their headquarters.

# Program:
18:00 Doors open. Come to mingle and have some food and drink!
18:45 Welome by Schiphol Group
18:50 Talks by Schiphol Group:
- Aircraft turnaround insights powered by Deep Learning (Jori van Lier)
- Schiphol Runway: take off to the cloud(s)! (Daniel van der Ende & Tim van Cann)
19:50 Talks by other company: TBA
- [TBA] (TBA)
20:20 More time to mingle and drink!
Around 21:15 the bar will close (and the building as well!)

# Abstracts and bio's
## Aircraft turnaround insights powered by Deep Learning
When an aircraft arrives at a gate at Schiphol, the turnaround process begins: de-boarding the passengers, unloading bags and cargo, cleaning, fueling, catering, etc. - all as fast as possible such that the aircraft can take off again with minimal delay. Many things can and do go wrong during this process. One of the top priorities at Schiphol is to improve On-Time Performance. Unfortunately, we were missing key datapoints of this process and were not able to analyse it effectively. In order to solve that, we have started to automatically process the video streams using Computer Vision and Deep Learning techniques. We are now able to recognise vehicles and other objects on the aircraft stand, and can determine whether key process milestones have started on time.

Bio Jori van Lier:
Jori van Lier is a Senior Data Scientist at Schiphol (ad interim). He worked on various data products and models to to predict and improve airport operations. Before Schiphol, he worked in the Big Data team at KPMG and he has a background in Computer Science (MSc).

## Schiphol Runway: take off to the cloud(s)!
Developing and deploying data products often involves several steps, especially when using cloud infrastructure. Tying this all together can take a lot of time, and can also result in a lot of different ways to achieve the same goal throughout an organisation. At Schiphol, we've built a library, dubbed Runway, that we use for deployment of
data products to the Schiphol Data Hub (SDH) on Microsoft Azure. Runway allows us to abstract away the details of how to interact with the various components and systems needed to put a full-fledged data product live. From deploying Spark jobs on Databricks, publishing custom packages to pypi, setting up Azure Eventhub consumers, deploying API's on Kubernetes, and much more. Runway also handles differentiating between deploying to development, acceptance, and production environments. Runway itself is written in Python, and we'll give you a glimpse into how we built it.

Bio Daniel van der Ende:
Daniel is a data engineer at GoDataDriven. Daniel enjoys working on high performance distributed computation with Spark, empowering data scientists by helping them run their models on very large datasets performantly. He's currently exploring deep learning, but from an engineering perspective; how to get this class of solution in production in a stable and reliable way. Daniel is an Apache Spark and Apache Airflow contributor and speaker at conferences and meetups. In his spare time, he enjoys video games and reading (both fiction and non-fiction) and can sometimes be found trying out new tech on his home-built server.

Bio Tim van Cann:
Tim is a data engineer at GoDataDriven. He has a background in Artificial Intelligence (MSc) and Software Development and as such enjoys building scalable machine learning solutions, feeling comfortable with both data science and data engineering.
His main focus is getting models to production to achieve business value.
Tim is often seen in the gym lifting weights. He also takes the occasional run, bike ride, swim or crossfit WOD. Ask him anything about food and/or fitness and you'll likely get a helpful answer.