Opportunities
Elastic Tech Briefing hosted by Clarity Business Solutions
Partner Clarity Business Solutions will be hosting an online event on Elastic on August 20th. Come learn how to make the most of The Elastic Stack and bring innovation and mission success to your programs.
Data Works MD Conference 2021
We are in the early planning stages for a Maryland data-focused conference in June 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 are looking to put together a group to focus on a few problems working with Baltimore City data and need your help. 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
GPT-3: Game-Changer? — The data science and machine learning world was abuzz about GPT-3, the OpenAI text generation neural network capable of amazing things. It has implications for NLP and could be a game-changer or a disaster. There is now a ton of information available such as a tool to explore GPT-3, repositories of cool demos, and quick-start 3-minute guides. But why does it matter? Should we temper expectations? What is the truth beyond the hype? And how does it work? Tags: Neural Networks, NLP
We Have Already Let The Genie Out of The Bottle — Great article from Tim O'Reilly about how we need to make sure that Artificial Intelligence won’t run amok and will be a force for good. Tags: Machine Learning
Best Data Science Books According to the Experts — Three experts to recommend their favorite data science books ranging from general interest to advanced textbooks. Tags: Machine Learning, Books
Experts Recommend Machine Learning Books — Dozens of experts and professionals in Machine Learning were asked about their favorite books – and here are the answers. Tags: Machine Learning, Books
Byte Down: Making Netflix’s Data Infrastructure Cost-Effective — Netflix's efficiency approach is to provide cost transparency and place the efficiency context as close to the decision-makers as possible. Their highest leverage tool is a custom dashboard that serves as a feedback loop to data producers and consumers — it is the single holistic source of truth for cost and usage trends for Netflix’s data users. This post details their approach and lessons learned in creating our data efficiency dashboard. Tags: Data, Infrastructure
Deep Learning to Translate Between Programming Languages — Facebook recently open-sourced TransCoder, an entirely self-supervised neural transcompiler system that can make code migration far easier and more efficient. Their method is the first AI system able to translate code from one programming language to another without requiring parallel data for training. Tags: Deep Learning
The Data Science Lifecycle Process — The Data Science Lifecycle Process is a set of prescriptive steps and best practices to enable data science teams to consistently deliver value. It includes issue templates for common data science work types, a branching strategy that fits the data science development flow, and prescriptive guidance on how to piece together all the various tools and workflows required to make data science work. Tags: Data Science
Data Science: The Key Tool Cities Need To Reduce Carbon Emissions — As the economy begins to reopen, cities must continue to use data to not only monitor Covid-19 but also initiate a sustainable recovery from the Covid-19 crisis. As cities will play an integral part in achieving a sustainable recovery, they need to capitalize on three data-driven solutions to incorporate more clean energy into the grid system, optimize the charging of electric vehicles, and reduce energy consumption in buildings. By doing so, countries will not only create highly skilled employment but also accelerate their transition towards a low-carbon economy. Tags: Data Science
Overcoming Bias In A World Of Bad Information — Artificial intelligence already outperforms judges in choices about setting bail because humans on the bench tend to overthink the defendants’ demeanor, a poor predictor of flight risk. Likewise, hiring algorithms do better than recruiters at screening resumes because humans in HR show too much favoritism for traditional applicants. Unfortunately, smart technology also has blind spots. Tags: Data
Do We Need Deep Graph Neural Networks? — One of the hallmarks of deep learning was the use of neural networks with tens or even hundreds of layers. In stark contrast, most of the architectures used in graph deep learning are shallow with just a handful of layers. In this post, I raise a heretical question: does depth in graph neural network architectures bring any advantage? Tags: Machine Learning, Visualization
Accelerating AI with Synthetic Data — The application of artificial intelligence and machine learning to solve today’s problems requires access to large amounts of data. One of the key obstacles faced by analysts is access to this data which may be sensitive and private. Synthetic data can help solve this data problem in a privacy-preserving manner. Tags: Testing
Things I Wished More Developers Knew About Databases — In data-heavy systems, databases are at the core of system design goals and tradeoffs. Even though it is impossible to ignore how databases work, the problems that application developers foresee and experience will often be just the tip of the iceberg. In this series, the author shares a few insights I specifically found useful for developers who are not specialized in this domain. Tags: Software Engineering
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How-To's and Tutorials
Getting Machine Learning to Production — Interesting article on how end-to-end machine learning works, starting from creation to deployment to the cloud. Tags: Machine Learning
How to Build Advanced SQL — SQL remains the language for data and is still worth knowing. But once you know the basics, how do you progress? What takes a SQL user from novice to advanced? Tags: Data, SQL, Training
TinyPilot: Build a KVM Over IP for Under $100 — TinyPilot is an inexpensive, open-source device for controlling computers remotely. This post walks through the creation of TinyPilot and shows how you can build your own for under $100 using a Raspberry Pi. Tags: Hardware
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Data Tools and Resources
Darts: Time Series Made Easy in Python — Time series simply represent data points over time. They are thus everywhere in nature and in business. Although there exist many models and tools for time series, they are still often nontrivial to work with, because they each have their own intricacies and cannot always be used in the same way. In this article, the author introduces Darts, a new attempt at simplifying time series processing and forecasting in Python. Tags: Tools, Time Series
SnapML — For years now, Snapchat has been at the forefront of mobile machine learning — their popular Lenses, which often combine on-device ML models with augmented reality, have become shining examples of the power and flexibility of on-device machine learning. Snap has now released SnapML to make it even easier to add models and custom neural networks directly to Snapchat. More information available. Tags: Machine Learning
Snorkel AI: Putting Data First in ML Development — Snorkel AI, which spun out of the Stanford AI Lab in 2019, was founded on two simple premises: first, that the labeled training data machine learning models learn from is increasingly what determines the success or failure of AI applications. And second, that we can do much better than labeling this data entirely by hand. Tags: Machine Learning, Data
Deep Learning Papers Reading Roadmap — If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" This roadmap covers the history, basics, models, deep transfer learning, and applications such as NLP and object detection. Tags: Machine Learning, Training
2020 Machine Learning Roadmap — An extensive roadmap covering the important concepts in machine learning. Covers problems, processes, tools. mathematics, and resources. The code is also available. Tags: Machine Learning, Training
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