What we're about

From using GPUs to process datasets faster to scaling up workloads dependent on single-node calculation engines in perfectly parallel mode with Spark, and from distributed in-memory filesystems for managing hot data to distributed processing of geospatial and time-series data, we typically cover the cases that need some custom designs and for which there are no out-of-the-box solutions on or off the cloud. Join us to learn and share with us what you know to get things done, faster and better. Technologies that we consider are typically open-source, with permissive licenses.

Meetup group members with names and introduction section properly filled out in their profiles will have priority in attending the meetups in case of limited capacity. Apart from attending the meetups, please do not hesitate to reach out to the organizer if you wish to present your work: an industrial project, an academic paper, or even an interesting work you've done as a hobby in the area of data processing/engineering. Anything technically interesting for our community can be scheduled in an upcoming meetup (a presentation between 5 and 60 minutes).

Each meetup comes with its own terms and conditions specified in its page, but there are also common terms that apply to all. By attending a meetup, you indicate that you have read, understood and accepted these terms: https://www.montrealml.dev/terms . The EDPP Montreal meetup group is the sister group to the ML meetup group, focused on data processing/engineering, and is being organized under the same terms.

Upcoming events (1)

Big Geospatial Data with Open-source Tech (Vectors, Rasters & Map-matching)

Maison du développement durable

Join us for the 2nd meetup of 2019! We have a main presentation on Big Geospatial Data and have an open slot for another presentation. Tentative schedule: 18h15-18h30: arrival/registration 18h15-18h45: soft drinks/snacks & networking 18h45: Main presentation/demo by Masood Krohy: Big Geospatial Data with Open-source Tech (Vectors, Rasters & Map-matching) 19h45: break 20h00-20h30: Open slot (contact the organizer) 20h30-21h30: networking/casual brainstorming Mandatory registration: https://forms.gle/5vuqnFUqoGFwoJnz9 Important notes at the bottom; please read in full. Main presentation: Big Geospatial Data with Open-source Tech (Vectors, Rasters & Map-matching) Summary: Geospatial datasets (i.e. geocoded data points) are everywhere nowadays and often add enormous value to data analytics/mining and machine learning projects. In this new era of Big Data, libraries and engines such as GeoPandas, PostGIS and the equivalent products in the commercial space often fall short and cannot scale up sufficiently to let us tap into the Big Data that is being collected in many use cases and by many organizations. In this talk/demo, we explore free, open-source, Big Data-ready technologies and workflows like GeoMesa, GeoPySpark and OSRM-on-Spark and show how to use these Apache Spark-based tech/workflows for key geospatial operations and use cases. We start by introducing GeoMesa and demo-ing how it can be used to ingest Big Geospatial Data and perform operations on vectors. Next, we briefly introduce GeoPySpark, the Python interface to Geotrellis, for performing operations on rasters. At the end, we turn to map-matching which is the process of associating names to geocoded data points from an underlying network (e.g., determining which street a particular GPS point should be associated with). We describe and demo how we can combine OSRM with Spark to do scalable map-matching on Big Data and therefore open up a lot of possibilities for advanced data mining and machine learning projects. Presenter Bio: Masood Krohy is a Data Science Platform Architect/Advisor and most recently acted as the Chief Architect of UniAnalytica, an advanced data science platform with wide, out-of-the-box support for time-series and geospatial use cases. He has worked with several corporations in different industries in the past few years to design, implement and productionize Deep Learning and Big Data products. He holds a Ph.D. in computer engineering. RSVP on this site, then confirm with form submission: after a Yes RSVP to temporarily reserve your seat, you have 24 hours to fill and send the above registration form; if not received within the delay, your RSVP may be changed to No (and your seat may be given automatically to the next in line). We use the responses to the above registration form to sign you in at the entry. This meetup group is used to facilitate networking between attendees. Complete profile advantage: meetup group members with names and introduction section properly filled out in their profiles will have priority in attending the meetup in case of limited capacity. The EDPP Montreal meetup group is the sister group to the ML meetup group, focused on data processing/engineering, and is being organized under the same terms; please see the important details common to our meetups posted at https://www.montrealml.dev/terms : - Overbooking: this is a free-to-attend event and, as such, there is some overbooking. More details are at the above URL - Photo/Video permission - Attendee selection process (in case of limited capacity)

Past events (1)

Photos (19)

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