Parts of this blog, including the code examples and architecture frameworks, were generated using Keyhole Software’s EnterpriseGPT web client—an open-source tool that empowers organizations to deploy user-friendly chatbot solutions with ease. Interested in exploring further? Contact our team, and we’d be happy to provide credentials for you to try it out. The Rise of Python and Other Languages We’ve seen …
Next Level with Matt Menzenski, Principal Data Engineer
Take your career to the “Next Level” with Zach Gardner, Chief Architect at Keyhole Software, featuring Matt Menzenski, Principal Data Engineer at PayIt. Matt shares his experiences coming from a non-traditional background into programming (starting in Slavic linguistic corpus analysis) for one of the largest providers in the US of software to municipalities. Matt gives his advice to the next …
[Video] Data Mining/Science: Supervised Time-Series Model
In this video, Alex presents an introduction to data science “big ideas” relevant to the model, an explanation of its data modeling process, and a demonstration of the real-life machine learning solution implemented with Python, Postgres SQL, and H2O (an open-source machine learning algorithm). Multiple data sources and technologies were combined to create an accurate model that allows farmers and recreationists to make actionable insights about the future. A few questions and answers round out the discussion.
A Vue of Python
Earlier this year I blogged about creating a Lean Mean Vue Machine called Quotes on Demand. The application was a fully featured CRUD application served from a NodeJS server and had a self contained VueJS front end.
But wouldn’t it be a nice test to see if that same Vue application could switch over to another API, say something like a Python web server powered by Flask?
In this post, we will create a Python web application that will have 100% parity to an existing NodeJS web application. This will enable an existing VueJS front end to connect to the application with no additional code changes in the user interface code.
How to Create a Dystopian Future at Home with Python, OpenCV, and Microsoft Azure
Facial recognition is both amazing and horrifying. Some amazing things it can do is the ability to find missing children or seniors, using your face to unlock your phone, and being able to board an airplane faster.
In this blog post, I want to highlight some powerful tools and platforms that allow you to create distributed facial recognition systems with OpenCV and Azure’s Cognitive Services. By the end of this post, you will have a working face detector using OpenCV that can communicate with Azure’s Cognitive Services.
I used Python 3.7.4 and pip 19.2.3 for this project. You can view the code from this blog at https://github.com/dcandre/Dystopian-Future-At-Home.
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