Opportunities
Data Works MD Conference 2021
We are in the early planning stages for a Maryland data-focused conference in 2021. If you would like to stay informed, please sign-up for updates.
Interested in a side project?
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? We have put together a group to focus on a few problems working with Baltimore City data and need your help. The current project focuses on data parsing and analysis for the Baltimore Board of Estimates. If interested, please send us an email or join us on Slack to discuss building a side project group.
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 the opportunities available with our partners.
Are you hiring?
If your company is looking for data scientists, data engineers, software engineers, and other data related experts, please reach out so that we can help our members find new opportunities.
Please send us an email introducing your company and needs.
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!
Erias Ventures
Erias has an immediate need for Software Engineers, System Engineers, Test Engineers, Data Scientists, and System Administrators. External referral bonuses are available. For more information, please contact us at info@eriasventures.com.
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Data News and Articles
AlphaFold: A Solution to a 50-year-old Grand Challenge in Biology — Figuring out what shapes proteins fold into is known as the “protein folding problem”, and has stood as a grand challenge in biology for the past 50 years. In a major scientific advance, the latest version of our AI system AlphaFold has been recognized as a solution to this grand challenge. Tags: AI, Deep Learning
Can Analytics Revolutionize High School Football? — While NCAA and professional teams are prohibited from using computers to guide in-game tactics, high school sports are not. Learn how local Mater Dei is using analytics and maybe changing how high school football. Tags: Sports
Analytics at Netflix: Who We Are and What We Do — When you think about data at Netflix, what comes to mind? Oftentimes it is their content recommendation algorithm or the online delivery of video to your device at home. This article focuses on Netflix’s Data Science and Engineering group, which specializes in analytics at scale. Tags: Analytics, Business
Emerging Architectures for Modern Data Infrastructure — As an industry, we’ve gotten exceptionally good at building large, complex software systems. We’re now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. We’re seeing quick-moving impacts of this trend across the industry, including the emergence of new roles, shifts in customer spending, and the emergence of new startups providing infrastructure and tooling around data. Tags: Infrastructure
Programming Language Python Is a Big Hit for ML. But Now It Needs To Change — Despite its popularity, Python could become limited to data science alone on its current trajectory, say two experts. Tags: Python
Organizing Data Teams: Where to Make The Cut — There are four ways to decentralize and structure data teams. Learn how to choose the right one. Tags: Team
2020 Trends to Watch — A collection of interesting articles on trends from 2020 including AI, robotics, infrastructure, security, and privacy. Tags: AI, Trends
2020’s Top AI & Machine Learning Research Papers — Despite the challenges of 2020, the AI research community produced a number of meaningful technical breakthroughs. GPT-3 by OpenAI may be the most famous, but there are definitely many other research papers worth your attention. Tags: AI, ML
Data Quality at Airbnb — Airbnb has transitioned from a startup moving at light speed to a mature organization with thousands of employees. During this transformation, Airbnb experienced the typical growth challenges that most companies do, including those that affect the data warehouse. This post explores the data challenges Airbnb faced during hyper growth and the steps they took to overcome these challenges. Tags: Data
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How-To's and Tutorials
Deep Learning in Production — This repository is a collection of useful notes and references about deploying deep learning-based models in production. Tags: Deel Learning
Become a Superlearner! An Illustrated Guide to Superlearning — Why use one machine learning algorithm when you could use all of them?! This post contains a step-by-step walkthrough of how to build a superlearner prediction algorithm in R. Tags: R
Coding for Sports Analytics: Resources to Get Started — These days, if you want to work in sports analytics, you need to know how to code. There’s really no way around it. And while that can be scary for someone who’s never written a line of code before, it’s not as daunting as it seems. Tags: Sports, Analytics
Get Started with Machine Learning on Arduino — Arduino is on a mission to make machine learning simple enough for anyone to use. In this article, learn how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager. Tags: Arduino, ML
Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression — When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible for storing and serving feature data. Tags: Redis
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Data Tools and Resources
Awesome Data Engineering — Learning path and resources to become a data engineer. Best books, best courses and best articles on each subject. Tags: Tools, Resources
Deepnote — Deepnote is a new kind of data science notebook. It's Jupyter-compatible, supports real-time collaboration, and runs in the cloud. Tags: Tools, Jupyter
ZenML — ZenML is an extensible, open-source MLOps framework for using production-ready Machine Learning pipelines - in a simple way. It includes features such as guaranteed reproducibility of training experiments, guaranteed comparability between experiments, and the ability to quickly switch between local and cloud environments. Tags: Tools, Data
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