November 2019

Upcoming Events

Robotics and Machine Learning: Working with NVIDIA Jetson Kits
December 17 | 6:30 PM | UMBC

Interested in machine learning and AI? Do you want to learn more about high performance GPU programming and how it applies to Deep Learning? Patty Delafuente will introduce you to the Nvidia Jetson developer kits, discuss their applications, how to get started, and provide a live demonstration of NVIDIA® Jetson Nano™, an easy to use deep learning and robotics platform.
For more information and to register, please click here.

Past Events

Connect Data and Devices with Apache NiFi | November 2019
Video available here.

Special Session: Introduction to Machine Learning | September 2019
Video available here.

Data in the City: Analytics and Civic Data in Baltimore | August 2019
Video available here.

Data Science Student Showcase: Towson Edition | May 2019
Video available here.

Accelerated Data Science: Analytic Pipelines with GPUs | March 2019
Video available here.

Detecting Noise: Using Weakly Supervised Algorithms with EEG Data | February 2019
Video available here.


Upcoming Baltimore Hackathon
Have an idea for Baltimore Hackathon taking place in Spring 2020? Submit your idea here

Considering a career change?
Are you a software or system engineer, data scientist, analytic developer, or cybersecurity expert interested in learning about new opportunities?
Please send us an email to learn about opportunities available with our sponsors and partners.

Interested in side projects?
Are you an expert with data and willing to mentor, or are you an up and coming hobbyist looking for a side project to work on?
If so, please send us an email to discuss building a side project group.

Get involved!
Want to be more involved in our data science community? If you have experience running workshops, hackathons, curating newsletters, or are just interested in helping to grow the meetup, please send us an email!

Data News and Articles



Can a Machine Learn to Write for The New Yorker?  Longform article about OpenAI and its ability to produce articles for the prestigious New Yorker.
For more, click here.

Continuous Delivery for Machine Learning  — Machine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application.
For more, click here.

DeepMind’s AI has now outcompeted nearly all human players at StarCraft II  — AlphaStar cooperated with itself to learn new strategies for conquering the popular galactic warfare game.
For more, click here.

A Tech Group Suggests Limits for the Pentagon’s Use of AI  The Defense Innovation Board, with members from Google, Microsoft, and Facebook, praises the power of military AI but warns of unintended harms or conflict.
For more, click here.

Even the AI Behind Deepfakes Can’t Save Us From Being Duped  — Deepfakes will most likely improve faster than detection methods, and because human intelligence and expertise will be needed to identify deceptive videos for the foreseeable future.
For more, click here.

Clustering NBA Playstyles Using Machine Learning — Can we use machine learning to place NBA players into categories to predict how a player fits in on a given team?
For more, click here.

Everything a Data Scientist Should Know About Data Management, But Were Afraid to Ask — While the bulk of data science training focuses on machine/deep learning techniques; data management knowledge is often treated as an afterthought. This article provides a roadmap of what a data scientist in 2019 should know about data management — from types of databases, where and how data is stored and processed, to the current commercial options.
For more, click here.

Getting Started with Data Lineage Do you know your data lake inside out? Are you keeping track of every change applied across all your tables and processes? Can you quickly teach a newcomer how to navigate between your different datasets? Building a “data lineage” platform could be a good start.
For more, click here.

How-To's and Tutorials


How to Build a Recommender Engine for Medical Research Papers — A step-by-step guide to building a recommender pipeline, from data wrangling to model evaluation.
For more information, click here.

Deep Learning with PyTorch: A 60 Minute Blitz — A tutorial covering PyTorch’s Tensor library and neural networks at a high level and showing how to train a small neural network to classify images.
For more information, click here.

Data Tools


Statistics Resources — Recommendations for where to learn statistics.
For more information, click here.

What You Need to Know About Netflix’s ‘Jupyter Killer’: Polynote — Today, Netflix open-sourced Polynote, the internal notebook they developed, to the public and it’s about time Jupyter Notebook has its worthy competitor.
For more information, click here.

Introducing Apache Arrow Flight: A Framework for Fast Data Transport — Over the last 18 months, the Apache Arrow community has been busy designing and implementing Flight, a new general-purpose client-server framework to simplify high performance transport of large datasets over network interfaces.
For more information, click here.

If you are interested in speaking, hosting, or sponsoring a meetup, have opportunities to list, or local news to share, please email

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Data Works MD · 101 W Dickman St · Baltimore, MD 21784-9239 · USA

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