Pinpoint Engineering

The TL;DR from my session on AI and EngOps at AIDe...

This morning I had the opportunity to chat with software engineers and data scientists at the AI Dev World Conference on a topic I just happen to be v...

The TL;DR from my session on AI and EngOps at AIDevWorld

The TL;DR from my session on AI and EngOps at AIDe...

This morning I had the opportunity to chat with software engineers and data scientists at the AI Dev World Conference on a topic I just happen to be very passio...

Our 12 go-to Python libraries for data science

We use data science — machine learning, natural language processing, etc. in Pinpoint to correlate data from all the tools used to build software to unlock acti...

Two data science life hacks to improve your workfl...

Data science is fundamental to Pinpoint’s application. But, like most startups, we are still in the process of building out our data science architecture; how w...

Pinpoint Book Club Reads: The Tyranny of Metrics

One of the most gratifying (and often frustrating) features of life in a software startup is the “everyone owns everything” philosophy. Borne of necessity as mu...

Building a Machine Learning Model to Forecast Cycl...

For engineering, the ability to accurately estimate how long an issue will take is crucial to properly setting expectations and delivering products on time. The...

Tailoring Data Models to Client Demands and Behavi...

At Pinpoint, we offer customers two types of data models for insights across our platform that leverage data science and machine learning.  Our team commonly us...

Building Data Science Applications with R: Takeawa...

One of our engineering goals this year at Pinpoint is to improve the entire team’s knowledge about the different methodologies and tools available for us to use...

Data, eh?: What I Learned As A Data Science Intern

I’m a creative, visually-oriented person, which is why I’m studying statistics with a focus on data visualization. When I saw an opportunity through the Univers...

Using machine learning to measure code risk

In software engineering, “risk” can mean a lot of things. When it comes to code risk, we mean the likelihood that any particular commit is going to add to your ...

How good are we? Hard questions meet hard data

Software engineering has more data about the way it works than probably any other organization. We’ve spent the last couple of decades automating nearly every p...

Understanding coding efficiency: the measures that...

We’ve discussed the value of looking at your software development process as a pipeline—doing so allows us to borrow methodology from manufacturing to measure t...

How we use machine learning for intelligent commit...

Just a few days ago, we wrote about four reasons to link your code commits to their originating issue. The idea is that remembering to link your code commits—ba...

Why data science isn't just Business Intelligence ...

Data: the stuff that anchors our decision-making in some kind of empirical reality, instead of pure instinct or gut feel.

Calculating the cost of legacy code

Technical debt is one of those broad engineering matters that everyone agrees should be addressed, but which contains so many flavors and interpretations that i...

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